- A3.4. Machine learning and statistics
- A3.4.1. Supervised learning
- A3.4.2. Unsupervised learning
- A3.4.3. Reinforcement learning
- A3.4.4. Optimization and learning
- A3.4.5. Bayesian methods
- A3.4.6. Neural networks
- A3.4.7. Kernel methods
- A3.4.8. Deep learning
- A6.1.2. Stochastic Modeling
- A6.1.3. Discrete Modeling (multi-agent, people centered)
- A6.2.2. Numerical probability
- A6.2.3. Probabilistic methods
- A6.2.4. Statistical methods
- A6.2.6. Optimization
- A6.3.3. Data processing
- A6.3.4. Model reduction
- A6.3.5. Uncertainty Quantification
- A6.4. Automatic control
- A6.4.1. Deterministic control
- A6.4.2. Stochastic control
- A6.4.3. Observability and Controlability
- A6.4.4. Stability and Stabilization
- A6.4.5. Control of distributed parameter systems
- A6.4.6. Optimal control
- A8.2.2. Evolutionary algorithms
- A8.11. Game Theory
- A9.2. Machine learning
- A9.3. Signal analysis
- A9.6. Decision support
- A9.7. AI algorithmics
- B1.1.2. Molecular and cellular biology
- B1.2.3. Computational neurosciences
- B2.5.1. Sensorimotor disabilities
- B4.2.1. Fission
1 Team members, visitors, external collaborators
- Pierre Del Moral [Inria, Senior Researcher, HDR]
- Emma Horton [Inria, Researcher]
- Dann Laneuville [DCNS Group]
- Francois Dufour [Team leader, Institut National Polytechnique de Bordeaux, Professor, HDR]
- Marie Chavent [Univ de Bordeaux, Associate Professor, HDR]
- Alexandre Genadot [Univ de Bordeaux, Associate Professor]
- Pierrick Legrand [Univ de Bordeaux, Associate Professor, HDR]
- Jerome Saracco [Institut National Polytechnique de Bordeaux, Professor, HDR]
- Huilong Zhang [Univ de Bordeaux, Associate Professor]
- Hadrien Lorenzo [Inria]
- Bastien Berthelot [UDcast, until Apr 2021]
- Tiffany Cherchi [UDcast, CIFRE, until Jun 2021]
- Alexandre Conanec [Bordeaux Sciences Agro, until Oct 2021]
- Alex Mourer [Groupe SAFRAN, CIFRE]
- Romain Namyst [Inria, from Apr 2021 until Sep 2021]
- Camille Palmier [ONERA, until Sep 2021]
- Nathanael Randriamihamison [Institut national de recherche pour l'agriculture, l'alimentation et l'environnement, until Sep 2021]
- Tara Vanhatalo [Orosys, from Apr 2021]
- Olivier Marceau [DCNS Group, Engineer]
- Romain Namyst [Inria, Engineer, from Oct 2021]
- Adrien Negre [DCNS Group, Engineer]
- Raymond Zhang [Inria, Engineer, Dec 2021]
Interns and Apprentices
- Mariette Dupuy [Institut Hospitalo-Universitaire de Bordeaux, from Mar 2021 until Aug 2021]
- Audrey Plaza [Inria, from Feb 2021]
2 Overall objectives
2.1 Outline of the research project
The highly interconnected contemporary world is faced with an immense range of serious challenges in statistical learning, engineering and information sciences which make the development of statistical and stochastic methods for complex estimation problems and decision making critical. The most significant challenges arise in risk analysis, in environmental and statistical analysis of massive data sets, as well as in defense systems. From both the numerical and the theoretical viewpoints, there is a need for unconventional statistical and stochastic methods that go beyond the current frontier of knowledge.
Our approach to this interdisciplinary challenge is based on recent developments in statistics and stochastic computational methods. We propose a work programme which will lead to significant breakthroughs in fundamental and applied mathematical research, as well as in advanced engineering and information sciences with industrial applications with a particular focus on defence applications, in collaboration with Naval Group.
Many real-world systems and processes are dynamic and essentially random. Examples can be found in many areas like communication and information systems, biology, geophysics, finance, economics, production systems, maintenance, logistics and transportation. These systems require dynamic and stochastic mathematical representations with discrete and/or continuous state variables in possibly infinite dimensional space. Their dynamics can be modeled in discrete or continuous time according to different time scales and are governed by different types of processes such as stochastic differential equations, piecewise deterministic processes, jump-diffusion processes, branching and mean field type interacting processes, reinforced processes and self-interacting Markov processes, to name a few. Our interdisciplinary project draws knowledge from information science, signal processing, control theory, statistics and applied probability including numerical and mathematical analysis. The idea is to work across these scientific fields in order to enhance their understandings and to offer an original theory or concept.
Our group mainly focuses on the development of advanced statistical and probabilistic methods for the analysis and the control of complex stochastic systems, as outlined in the following three topics.
- Statistical and Stochastic modeling: Design and analysis of realistic and tractable statistical and stochastic models, including measurement models, for complex real-life systems taking into account various random phenomena. Refined qualitative and quantitative mathematical analysis of the stability and the robustness of statistical models and stochastic processes.
- Estimation/Calibration: Theoretical methods and advanced computational methodologies to estimate the parameters and the random states of the model given partial and noisy measurements as well as statistical data sets. Refined mathematical analysis of the performance and the convergence of statistical and stochastic learning algorithms.
- Decision and Control: Theoretical methods and advanced computational methodologies for solving regulation and stochastic optimal control problems, including optimal stopping problems and partially observed models. Refined mathematical analysis of the long time behavior and the robustness of decision and control systems.
These three items are by no means independent.
- Regarding the interdependence between the modeling aspects and the estimation/calibration/control aspects, it must be emphasized that when optimizing the performance of a partially observed/known stochastic system, the involved mathematical techniques will heavily depend on the underlying mathematical characteristics and complexity of the model of the state process and the model of the observation process. The main difficulty here is to find a balance between complexity and feasibility/solvability. The more sophisticated a model is, the more complicated the statistical inference and optimization problems will be to solve.
- The interdependence that arises between estimation/calibration and the optimal control can be summarised as follows. When the decision-maker has only partial information on the state process, it is necessary to assume that the admissible control policies will depend on the filtration generated by the observation process. This is a particularly difficult optimisation problem to solve. Roughly speaking, by introducing the conditional distribution of the state process, the problem can then be reformulated in terms of a fully observed control problem. This leads to a separation of estimation and control principle, i.e. the estimation step is carried out first and then the optimisation. The price to be paid for this new formulation is an enlarged state space of infinite dimension. More precisely, in addition to the observable part of the state, a probability distribution enters the new state space which defines the conditional distribution of the unobserved part of the state given the history of the observations.
Solving such global optimization problems remain an open problem and is recognized in the literature as a very difficult challenge to meet.
One of the fundamental challenges we will address is to develop estimation/calibration and optimal control techniques related to general classes of stochastic processes in order to deal with real-world problems. Our research results will combine, mathematical rigour (through the application of advanced tools from probability, statistics, measure theory, functional analysis and optimization) with computational efficiency (providing accurate and applicable numerical methods with a refined analysis of the convergence). Thus, the results that we will obtain in this research programme will be of interest to researchers in the fields of stochastic modeling, statistics and control theory both for the theoretical and the applied communities. Moreover, the topics studied by Naval Group, such as target detection, nonlinear filtering, multi-object tracking, trajectory optimization and navigation systems, provide a diverse range of application domains in which to implement and test the methodologies we wish to develop.
The final goal is to develop a series of reliable and robust softwares dedicated to statistical and stochastic learning, as well as automated decision and optimal control processes. The numerical codes are required to be both accurate and fast since they are often elements of real time estimation and control loops in automation systems. In this regard, the research topics proposed by Naval Group will provide a natural framework for testing the efficiency and robustness of the algorithms developed by the team.
From our point of view, this collaboration between the INRIA project team and Naval Group offers new opportunities and strategies to design advanced cutting-edge estimation and control methodologies.
2.2 Approach and methodologies
The types of learning and control methodologies developed by the team differ in their approach as well as in the problems that they are intended to solve. They can be summarised by the following three sets of interdependent methodologies.
- Statistical learning: Regression, clustering, volume and dimensionality reduction, classification, data mining, training sets analysis, supervised and unsupervised learning, active and online learning, reinforcement learning, identification, calibration, Bayesian inference, likelihood optimisation, information processing and computational data modeling.
- Stochastic learning: Advanced Monte Carlo methods, reinforcement learning, local random searches, stochastic optimisation algorithms, stochastic gradients, genetic programming and evolutionary algorithms, interacting particle and ensemble methodologies, uncertainty propagation, black box inversion tools, uncertainty propagation in numerical codes, rare event and default tree simulation, nonlinear and high dimensional filtering, prediction and smoothing.
- Decision and control: Markov decision processes, piecewise deterministic Markov processes, stability, robustness, regulation, optimal stopping, impulse control, stochastic optimal control including partially observed problems, games, linear programming approaches, dynamic programming techniques.
All team members of the project work at the interface of the these three areas. This joint research project between INRIA and Naval Group is a natural and unprecedented opportunity to embrace and push the frontiers of the applied and theoretical sides of these research topics in a common research team.
Despite some recent advances, the design and the mathematical analysis of statistical and stochastic learning tools, as well as automated decision processes, is still a significant challenge. For example, since the mid-1970s nonlinear filtering problems and stochastic optimal control problems with partial observations have been the subject of several mathematical studies, however very few numerical solutions have been proposed in the literature.
Conversely, since the mid-1990s, there has been a virtual explosion in the use of stochastic particle methods as powerful tools in real-word applications of Monte Carlo simulation; to name a few, particle filters, evolutionary and genetic algorithms and ensemble Kalman filters. Most of the applied research in statistics, information theory and engineering sciences seems to be developed in a completely blind way with no apparent connections to the mathematical counterparts.
This lack of communication between the fields often produces a series of heuristic techniques often tested on reduced or toy models. In addition, most of these methodologies do not have a single concrete industrial application nor do they have any connection with physical problems.
As such, there exists a plethora of open mathematical research problems related to the analysis of statistical learning and decision processes. For instance, a variety of theoretical studies on particle algorithms, including particle filters and sequential Monte Carlo models are often based on ad-hoc and practically unrealistic assumptions for the kinds of complex models that are increasingly emerging in applications.
The aim of this project is to fill these gaps with an ambitious programme at the intersection of probability, statistics, engineering and information sciences.
One key advantage of the project is its interdisciplinary nature. Combining techniques from pure and applied mathematics, applied probability and statistics, as well as computer science, machine learning, artificial intelligence and advanced engineering sciences enables us to consider these topics holistically, in order to deal with real industrial problems in the context of risk management, data assimilation, tracking applications and automated control systems. The overarching aim of this ambitious programme is to make a breakthrough in both the mathematical analysis and the numerical aspects of statistical learning and stochastic estimation and control.
2.3 Innovation and industrial transfer
Fundamentally, our team is not driven by a single application. The reasons are three-fold. Firstly, the robustness and transferability of our approaches means that the same statistical or stochastic learning algorithms can be used in a variety of application areas. On the other hand every application domain offers a series of different perspectives that can be used to improve the design and performances of our techniques and algorithms. Last but not least, industrial applications, including those that arise in defence, require specific attention. As such, we use a broad set of stochastic and statistical algorithms to meet these demands.
This research programme is oriented towards concrete applications with significant potential industrial transfers on three central problems arising in engineering and information and data sciences, namely, risk management and uncertainty propagation, process automation, and data assimilation, tracking and guidance. Our ultimate goal is to bring cutting edge algorithms and advanced statistical tools to industry and defence. The main application domains developed by the team are outlined below:
- Risk management and uncertainty propagation: Industrial and environmental risks, fault diagnostics, phase changes, epidemiology, nuclear plants, financial ruin, systemic risk, satellite debris collisions.
- Process automation: Production maintenance and manufacturing planing, default detection, integrated dynamics and control of distributed dynamical systems, multi-object coordination, automatic tuning of cochlear implants, classification of EGG signals.
- Data assimilation, tracking and guidance: Target detection and classification, nonlinear filtering and multi-object tracking, multiple sensor fusions, motion planning, trajectory optimization, design of navigation systems.
The main objectives and challenges related to the three application domains discussed above will be developed in section 4. The latter application domain will be developed in collaboration with Naval Group. The reader is refereed to section 4.1 for a description of this collaboration and to sections 3.2 and 3.3 for the theoretical aspects that will be carried out by the team in relation to these topics. Specific details on the particular techniques used to tackle the estimation and tracking problems in the context of the collaboration with Naval Group will remain confidential.
3 Research program
This section describes the different challenges we intend to address in the theoretical and numerical aspects of statistical/stochastic learning and optimal control. It will be difficult to convey the full complexity of the various topics and to provide a complete overview through a detailed timetable. Nevertheless, we will explain our motivation and why we think it is imperative to address these challenges. We will also highlight the technical issues inherent to these challenges, as well as the difficulties we might expect.
We are confident that the outcomes of this scientific project will lead to significant breakthroughs in statistical/stochastic learning and optimal control with a special emphasis on applications in the defence industry in collaboration with Naval Group. In this respect, we would like to quote Hervé Guillou, CEO of Naval Group, on the occasion of the signing of the partnership agreement between INRIA and Naval Group on December 10, 2019: ”This partnership will enable Naval Group to accelerate its innovation process in the fields of artificial intelligence, intelligence applied to cyber and signal processing. This is a necessity given the French Navy's need for technological superiority in combat and the heightened international competition in the naval defence field...”
One of our greatest achievements would undoubtedly be to meet these challenges with Naval Group, particularly those related to the fields of statistical/stochastic learning and control. We could not dream of a better outcome for our project.
3.1 Statistical learning
Permanent researchers: M. Chavent, P. Del Moral, F. Dufour, A. Genadot, E. Horton, P. Legrand, J. Saracco.
Regarding statistical learning, some of the objectives of the team is to develop dimension reduction models, data visualization, non-parametric estimation methods, genetic programming and artificial evolution. These models/methodologies provide a way to understand and visualize the structure of complex data sets. Furthermore, they are important tools in several different areas of research, such as data analysis and machine learning, that arise in many applications in biology, genetics, environment and recommendation systems. Of particular interest is the analysis of classification and clustering approaches and semi-parametric modeling that combines the advantages of parametric and non-parametric models, amongst others. One major challenge is to tackle both the complexity and the quantity of data when working on real-world problems that emerge in industry or other scientific fields in academia. Of particular interest is to find ways to handle high-dimensional data with irrelevant and redundant information.
Another challenging task is to take into account successive arrivals of information (data stream) and to dynamically refine the implemented estimation algorithms, whilst finding a balance between the estimation precision and the computational cost. One potential method for this is to project the available information into suitably chosen lower dimensional spaces.
For regression models, sliced inverse regression (SIR) and related approaches have proven to be highly efficient methods for modeling the link between a dependent variable (which can be multidimensional) and multivariate covariates in several frameworks (data stream, big data, etc.). The underlying regression model is semi-parametric (based on a single index or on multiple indices that allow dimension reduction). Currently, these models only deal with quantitative covariates. One of the team’s goals is to extend these regression models to mixed data, i.e. models dealing with quantitative and categorical covariates. This generalization would allow one to propose discriminant analysis to deal with mixed data. Extension of sparse principal component analysis (PCA) to mixed data is also another challenge. One idea is to take inspiration from the underlying theory and method of recursive SIR and SIR approaches for data stream in order to adapt them to commonly used statistical methods in multivariate analysis (PCA, discriminant analysis, clustering, etc.). The common aim of all these approaches is to estimate lower dimensional subspaces whilst minimizing the loss of statistical information. Another important aspect of data stream is the possible evolution in time of the underlying model: we would like to study break(s) detection in semi-parametric regression model, the breakdown being susceptible to appear in the parametric part or in the functional part of the regression model. The question of selecting covariates in regression modelling when we deal with big data is a fundamental and difficult problem. We will address this challenge using genetic programming and artificial evolution. Several directions are possible: for instance, improve, via genetic algorithms, the exploration of the covariate space in closest submodel selection (CSS) method or study optimization problems that simultaneously take into account variable selection, efficiency of estimation and interpretability of the model. Another important question concerns the detection of outliers that will disturb the estimation of the model, and this is not an obvious problem to deal with when working with large, high dimensional data.
In multivariate data analysis, an objective of the team is to work on a new formulation/algorithm for group-sparse block PCA since it is always important to take into account group information when available. The advantage of the group-sparse block PCA is that, via the selection of groups of variables (based on the synthetic variables), interpretability of the results becomes easier. The underlying idea is to address the simultaneous determination of group-sparse loadings by block optimization, and the correlated problem of defining explained variance for a set of non-orthogonal components. The team is also interested in clustering of supervised variables, the idea being to construct clusters made up of variables correlated with each other, which are either well-linked or not-linked to the variable to be explained (which can be quantitative or qualitative).
Another way to study the links between variables is to consider conditional quantiles instead of conditional expectation as is the case in classical regression models. Indeed, it is often of interest to model conditional quantiles, particularly in the case where the conditional mean fails to take into account the impact of the covariates on the dependent variable. Moreover, the quantile regression function provides a much more comprehensive picture of the conditional distribution of a dependent variable than the conditional mean function. The team is interested in the non parametric estimation of conditional quantile estimation. New estimators based on quantization techniques have been introduced and studied in the literature for univariate conditional quantiles and multivariate conditional quantiles. However, there are still many open problems, such as combining information from conditional quantiles of different orders in order to refine the estimation of a conditional quantile of a given order.
Another topic of interest is genetic programming (GP) and Artificial Evolution. GP is an evolutionary computation paradigm for automatic program induction. GP has produced impressive results but there are still some practical limitations, including its high computational cost, overfitting and excessive code growth. Recently, many researchers have proposed fitness-case sampling methods to overcome some of these problems, with mixed results in several limited tests. Novelty Search (NS) is a unique approach towards search and optimization, where an explicit objective function is replaced by a measure of solution novelty. While NS has been mostly used in evolutionary robotics, the team would like to explore its usefulness in classic machine learning problems.
Another important objective of the team is to implement new R (Matlab/Python) packages or to enrich those existing in the literature with the methods we are going to develop in order to make them accessible to the scientific community.
With respect to our statistical learning research program, the objectives of the team can be divided into mid- and long-term works. Mid-term objectives focus on sparsity in SIR (via soft thresholding for instance) and group-sparse block PCA, the underlying idea being to make the selection of variables or blocks of variables in the regression model or in the data. Taking into account multi-block data in regression models via data-driven sparse partial least squares is also at the heart of our concerns. Coupling genetic algorithms and artificial evolution with statistical modeling issues is also planned. The team has several long-term projects associated with the notion of data stream. Many theoretical and practical problems arise from the possible evolution of the information contained in the data: break detection in the underlying model, balance between precision and computational cost. Another scientific challenge is to extend certain approaches such as SIR to the case of mixed data by incorporating the information provided by the qualitative variables in the associated low dimensional subspaces. Moreover, the team has already worked on clustering of variables for mixed data and the clustering of supervised variables is now planned. Finally the idea of combining information from conditional quantiles of different orders in order to refine the estimation of a given order conditional quantile is still relevant today. It should be noted that other research themes may appear or become a priority depending on the academic or industrial collaborations that may emerge during the next evaluation period.
Some topics of the INRIA project teams (STATIFY, CELESTE, MODAL, SEQUEL, CLASSIC) are close to the ASTRAL objectives such as non parametric view of high dimensional data, statistical/machine learning, model selection, clustering, sequential learning algorithms, or multivariate data analysis for complex data. While certain ASTRAL objectives are similar to those of these teams, our approaches are significantly different. For example, in multivariate data analysis of complex data including clustering, our team mainly focuses on a geometric approach for mixed data. We also consider the case of successive arrivals of information in SIR both from the theoretical and numerical point of view. Currently there is no direct competition between our team and other INRIA project teams. However, interactions between ASTRAL and other INRIA teams exist. For instance, ASTRAL and STATIFY collaborations are fruitful with common publications, in particular with S. Girard (STATIFY project team).
In the field of multivariate data analysis, the team have interesting discussions with Agrocampus Ouest (Rennes, France) and with H.A.L. Kiers (Groningen University) on a mixed data approach for dimension reduction. Conditional and regression quantiles are very active research fields in France (University of Toulouse, Toulouse School of Economics, University of Montpellier) and around the world (ULB, Belgium; University of Illinois Urbana-Champaign, USA; Open University, UK; Brunel University, UK). The ASTRAL team has for the last four-year period collaborated with D. Paindaveine (ULB, Belgium). In the dimension reduction framework, there is a large international community in Europe, America or Asia working on SIR and related methods. However, to our knowledge, the ASTRAL team was the first to introduce importance of variables and recursive methods in SIR, and the first to adapt the SIR approach to data stream.
3.2 Stochastic learning
Permanent researchers: M. Chavent, P. Del Moral, F. Dufour, A. Genadot, E. Horton, D. Laneuville, P. Legrand, A. Nègre, J. Saracco, H. Zhang.
Stochastic particle methodologies have become one of the most active intersections between pure and applied probability theory, Bayesian inference, statistical machine learning, information theory, theoretical chemistry, quantum physics, financial mathematics, signal processing, risk analysis, and several other domains in engineering and computer sciences.
Since the mid-1990s, rapid developments in computer science, probability and statistics have led to new generations of interacting particle learning/sampling type algorithms, such as:
Particle and bootstrap filters, sequential Monte Carlo methods, self-interacting and reinforced learning schemss, sequentially interacting Markov chain Monte Carlo, genetic type search algorithms, island particle models, Gibbs cloning search techniques, interacting simulated annealing algorithms, importance sampling methods, branching and splitting particle algorithms, rare event simulations, quantum and diffusion Monte Carlo models, adaptive population Monte Carlo sampling models, Ensemble Kalman filters and interacting Kalman filters.
Since computations are nowadays much more affordable, the aforementioned particle methods have become revolutionary for solving complex estimation and optimization problems arising in engineering, risk analysis, Bayesian statistics and information sciences. The books 40, 44, 47, 62 provide a rather complete review on these application domains.
These topics have constituted some of the main research axes of several of the ASTRAL team members since the beginning of the 1990s. To the best of our knowledge, the first rigorous study on particle filters and the convergence of genetic algorithms as the size of the population tends to infinity seems to be the article 46, published in 1996 in the journal Markov Processes and Related Fields. This paper has opened an avenue of research questions in stochastic analysis and particle methods applications. The uniform convergence of particle filters and ensemble Kalman filters with respect to the time horizon was first seen in 41, 42, 45 and in the more recent article 48. The first use of particle algorithms and Approximated Bayesian Computation type methodologies in nonlinear filtering seems to have started in 43. Last but not least, the development of sequential Monte Carlo methodology in statistics was introduced in the seminal article 39.
Despite some recent advances, the mathematical foundation and the design and the numerical analysis of stochastic particle methods is still a significant challenge. For instance, particle filter technology is often combined with Metropolis-Hastings type techniques, or with Expectation Maximization type algorithms. The resulting algorithms are intended to solve high dimensional hidden Markov chain problems with fixed parameters. In this context (despite some recent attempts) the refined convergence analysis of the resulting particle algorithms, including exponential concentration estimates, remains to be developed.
Last but not least, the expectations of their performances are constantly rising in a variety of application domains. These particle methodologies are now expected to deal with increasingly sophisticated models in high dimensions, whilst also allowing for the variables to evolve at different scales. The overarching aim of this aspect of the programme is to make a breakthrough in both the mathematical analysis and the numerical simulation of stochastic and interacting particle algorithms.
Today, partly because of the emergence of new mean field simulation methodologies and partly because of the importance of new and challenging high-dimensional problems arising in statistical machine learning, engineering sciences and molecular chemistry, we are observing the following trends:
A need to better calibrate their performance with respect to the size of the systems and other tuning parameters, including cooling decay rates, local random search strategies, interacting and adaptive search criteria, and population size parameters. One of the main and central objectives is to obtain uniform and non asymptotic precision estimates with respect to the time parameter. These types of uniform estimates need to be developed, supporting industrial goals of enhanced design and confidence of algorithms, risk reduction and improved safety.
A need for new stochastic and adaptive particle methods for solving complex estimation models. Such models arise in a range of application areas including forecasting, data assimilation, financial risk management and analysis of critical events. This subject is also crucial in environmental studies and in the reliability analysis of engineering automated systems. The complexity of realistic stochastic models in advanced risk analysis requires the use of sophisticated and powerful stochastic particle models. These models go far beyond Gaussian models, taking into account abrupt random changes, as well as non nonlinear dynamics in high dimensional state spaces.
A need to find new mathematical tools to analyze the stability and robustness properties of sophisticated, nonlinear stochastic models involving space-time interaction mechanisms. Most of the theory on the stability of Markov chains is based on the analysis of the regularity properties of linear integral semigroups. To handle these questions, the interface between the theory of nonlinear dynamical systems and the analysis of measure valued processes needs to be further developed.
From a purely probabilistic point of view, the fundamental and the theoretical aspects of our research projects are essentially based on the stochastic analysis of the following three classes of interacting stochastic processes: Spatial branching processes and mean-field type interacting particle systems, reinforced and self-interacting processes, and finally random tree based search/smoothing learning processes.
The first class of particle models includes interacting jump-diffusions, discrete generation models, particle ensemble Kalman filters and evolutionary algorithms. This class of models refers to mean field type interaction processes with respect to the occupation measure of the population. For instance genetic-type branching-selection algorithms are built on the following paradigm: when exploring a state space with many particles, we duplicate better fitted individuals while particles with poor fitness die. The selection is made by choosing randomly better fitted individuals in the population. Our final aim is to develop a complete mean-field particle theory combining the stability properties of the limiting processes as the size of the system tends to infinity with the performance analysis of these particle sampling tools.
The second class of particle models refers to mean field type interaction processes with respect to the occupation measure of the past visited sites. This type of reinforcement is observed frequently in nature and society, where "beneficial" interactions with the past history tend to be repeated. Self interaction gives the opportunity to build new stochastic search algorithms with the ability to, in a sense, re-initialize their exploration from the past, re-starting from some better fitted previously visited initial value. In this context, we plan to explore the theoretical foundations and the numerical analysis of continuous time or discrete generation self-organized systems by combining spatial and temporal mean field interaction mechanisms.
The last generation of stochastic random tree models is concerned with biology-inspired algorithms on paths and excursions spaces. These genealogical adaptive search algorithms coincide with genetic type particle models in excursion spaces. They have been successfully applied in generating the excursion distributions of Markov processes evolving in critical and rare event regimes, as well as in path estimation and related smoothing problems arising in advanced signal processing. The complete mathematical analysis of these random tree models, including their long time behavior, their propagation of chaos properties, as well as their combinatorial structures are far from complete.
Our research agenda on stochastic learning is developed around the applied mathematical axis as well as the numerical perspective, including concrete industrial transfers with a special focus on Naval Group. From the theoretical side, mid-term objectives are centered around non asymptotic performance analysis and long time behavior of Monte Carlo methods and stochastic learning algorithms. We also plan to further develop the links with Bayesian statistical learning methodologies and artificial intelligence techniques, including the analysis of genetic programming discussed in section 3.1. We also have several long term projects. The first one is to develop new particle type methodologies to solve high dimensional data assimilation problems arising in forecasting and fluid mechanics, as well as in statistical machine learning. We also plan to design stochastic filtering-type algorithms to solve partially observed control problems such as those discussed in section 3.3.
In the last three decades, the use of Feynman-Kac type particle models has been developed in variety of scientific disciplines, including in molecular chemistry, risk analysis, biology, signal processing, Bayesian inference and data assimilation.
The design and the mathematical analysis of Feynman-Kac particle methodologies has been one of the main research topics of P. Del Moral since the late 1990's 46, 43, 41, see also the books 45, 40, 44, 47 and references therein. These mean field particle sampling techniques encapsulate particle filters, sequential Monte Carlo methods, spatial branching and evolutionary algorithms, Fleming-Viot genetic type particles methods arising in the computation of quasi-invariant measures and simulation of non absorbed processes, as well as diffusion Monte Carlo methods arising in numerical physics and molecular chemistry. The term "particle filters" was first coined in the article 46 published in 1996 in reference to branching and mean field interacting particle methods used in fluid mechanics since the beginning of the 1960s. This article presents the first rigorous analysis of these mean field type particle algorithms.
The INRIA project teams applying the particle methodology developed by ASTRAL include the INRIA project team SIMSMART (rare event simulation as well as particle filters) and the INRIA project team Matherials (applications in molecular chemistry). The project team ASTRAL also has several collaborative research projects with these, teams as well as with researchers from international universities working in this subject, including Oxford, Cambridge, New South Wales Sydney, UTS, Bath, Warwick and Singapore Universities.
3.3 Decision and stochastic control
Permanent researchers: P. Del Moral, F. Dufour, A. Genadot, E. Horton, D. Laneuville, O. Marceau, A. Nègre, J. Saracco, H. Zhang.
Part of this research project is devoted to the analysis of stochastic decision models. Many real applications in dynamic optimization can be, roughly speaking, described in the following way: a certain system evolves randomly under the control of a sequence of actions with the objective to optimize a performance function. Stochastic decision processes have been introduced in the literature to model such situations and it is undoubtedly their generic capacity to model real life applications that leads to and continues to contribute to their success in many fields such as engineering, medicine and finance.
In this project we will focus on specific families of models that can be identified according to the following elements: the nature of the time variable (discrete or continuous), the type of dynamics (piecewise deterministic trajectories) and the numbers of decision makers. For one player, the system will be called a stochastic control process and for the case of several decision-makers, the name (stochastic) game will be used. For ease of understanding, we now provide an informal description of the classes of stochastic processes we are interested in, according to the nature of the time variable.
In this framework, the basic model can be described by a state space where the system evolves, an action space, a stochastic kernel governing the dynamic and, depending on the state and action variables, a one-step cost (reward) function. The distribution of the controlled stochastic process is defined through the control policy which is then selected in order to optimize the objective function. This is a very general model for dynamic optimization in discrete-time, which also goes by the name of stochastic dynamic programming. For references, the interested reader may consult the following books 34, 36, 49, 50, 52, 53, 54, 55, 59, 58, 61 and the references therein (this list of references is, of course, not exhaustive).
Most of the continuous-time stochastic processes consist of a combination of the following three different ingredients: stochastic jumps, diffusion and deterministic motions. In this project, we will focus on non-diffusive models, in other words, stochastic models for which the randomness appears only at fixed or random times, i.e. those combining deterministic motions and random jumps. These stochastic processes are the so-called piecewise deterministic Markov processes (PDMPs) 35, 37, 38, 51, 56, 57, 60. This family of models plays a central role in applied probability because it forms the bulk of models in many research fields such as, e.g. operational research, management science and economy and covers an enormous variety of applications.
These models can be framed in several different forms of generality, depending on their mathematical properties such as the type of performance criterion, full or incomplete state information, with or without constraints, adaptative or not, but more importantly, the nature of the boundary of the state space, the type of dynamic between two jumps and on the number of decision-makers. These last three characteristics make the analysis of the controlled process much more involved.
Part of this project will cover both theoretical and numerical aspects of stochastic optimal control. It is clear that stochastic problems and control games have been extensively studied in the literature. Nevertheless, important challenges remain to be addressed. From the theoretical side, there are still many technical issues that are, for the moment, still unanswered or at most have received partial answers. This is precisely what makes them difficult and requires either the creative transposition of pre-existing methodologies or the development of new approaches. It is interesting to note that one of the feature of these theoretical problems is that they are closely related to practical issues. Solving such problems not only gives rise to challenging mathematical questions, but also allow a better insight into the structure and properties of real practical problems. Theory for applications will be for us the thrust that will guide us in this project. From the numerical perspective, solving a stochastic decision model remains a critical issue. Indeed, except for very few specific models, the determination of an optimal policy and the associated value function is an extremely difficult problem to tackle. The development of computational and numerical methods to get quasi-optimal solutions is, therefore, of crucial importance to demonstrate the practical interest of stochastic decision model as a powerful modeling tool. During the International Conference on Dynamic Programming and Its Applications held at the University of British Columbia, Canada in April 1977, Karl Hinderer, a pioneer in the field of stochastic dynamic programming emphasized that "whether or not our field will have a lasting impact on science beyond academic circles depends heavily on the success of implemented applications". We believe that this statement is still in force some forty years later.
The objective of this project is to address these important challenges. They are mainly related to models with general state/action spaces and with continuous time variables covering a large field of applications. Here is a list of topics we would like to study: games, constrained control problems, non additive types of criteria, numerical and computational challenges, analysis of partially observed/known stochastic decision processes. This list is not necessarily exhaustive and may of course evolve over time.
Our research agenda on optimal stochastic control is developed around the applied mathematical axis as well as the numerical perspective, including concrete industrial transfers with a special focus on Naval Group. Our mid-term objectives will focus on the following themes described above: properties of control policies in continuous-time control problems, non additive types of criteria, numerical and computational challenges. Our long-term objectives will focus on the analysis of partially observed/known stochastic control problems, constrained control problems and games.
There exists a large national/international community working on PDMPs and MDPs both on the theoretical, numerical and practical aspects. One may cite A. Almudevar (University of Rochester, USA), E. Altman (INRIA Team NEO, France), K. Avrachenkov (INRIA Team NEO, France), N. Bauerle (Karlsruhe University, Germany), D. Bertsekas (Massachusetts Institute of Technology, USA), O. Costa (Sao Paulo University, Brazil), M. Davis (Imperial College London, England), E. Feinberg (Stony Brook University, USA), D. Goreac (Université Paris-Est Marne-la-Vallée, France), X. Guo (Zhongshan University, China), O. Hernandez-Lerma (National Polytechnic Institute, Mexico), S. Marcus (University of Maryland, USA), T. Prieto-Rumeau (Facultad de Ciencias, UNED, Spain), A. Piunovskiy (University of Liverpool, England), U. Rieder (Universität Ulm, Germany), J. Tsitsiklis (Massachusetts Institute of Technology, USA), B. Van Roy (Stanford University, USA), O. Vega-Amaya (Universidad de Sonora, Mexico), Y. Zhang (University of Liverpool, England) to name just a few. Many of the colleagues cited above are at the head of research groups which have not been described in detail due to space limitation and so, this list is far from being exhaustive.
To some extent, our team is in competition wit the colleagues and teams mentioned above. We emphasize that there exists a long standing collaboration between our group and O. Costa (Sao Paulo University, Brazil) since 1998. In the last 10 years, we have established very fruitful collaborations with T. Prieto-Rumeau (Facultad de Ciencias, UNED, Spain) and A. Piunovskiy (University of Liverpool, England).
Inside INRIA, the team NEO and in particular E. Altman and K. Avrachenkov work on discrete-time MDPs but they are mainly focused on the case of countable (finite) state/action spaces MDPs. From this point of view, our results on this theme may appear complementary to theirs.
4 Application domains
It is important to point out that (for the time being) only a sub-group of the academic part of the team collaborates with Naval Group. Initially the topics of interest for Naval Group was focused on filtering and control problems. The academic members of this sub-group are P. Del Moral, F. Dufour, A. Genadot, E. Horton and H. Zhang. It is also important to emphasize that Naval Group is undoubtedly our privileged industrial partner. This collaboration is described in section REFERENCE NOT FOUND: ASTRAL-RA-2021/label/Naval Group-description. For reasons of confidentiality, this section is not very detailed, in particular it does not mention the timetable and does not detail the technical solutions that will be considered. Our aim in the short term is to integrate the remaining academic team members into the group to work on the themes of interest to NG. A seminar was organized for this purpose in August 2020. The academic members of the team who are not involved in collaboration with NG (M. Chavent, P. Legrand and J. Saracco) have their own industrial collaborations that are described in section 4.2.
4.1 Naval Group research activities
Permanent researchers: P. Del Moral, F. Dufour, A. Genadot, E. Horton, D. Laneuville, O. Marceau, A. Nègre and H. Zhang.
An important line of research of the team is submarine passive target tracking. This is a very complicated practical problem that combines both filtering and stochastic control topics. In the context of passive underwater acoustic warfare, let us consider a submarine, called the observer, equipped with passive sonars collecting noisy bearing-only measurements of the target(s). The trajectory of the observer has to be controlled in order to satisfy some given mission objectives. These can be, for example, finding the best trajectory to optimize the state estimation (position and velocity) of the targets, maximize the different targets' detection range and/or minimize its own acoustic indiscretion with respect to these targets, and reaching a way-point without being detected. Let us now describe in more detail some of the topics we intend to work on.
In the case of passive tracking problems, one of the main issues is that the observer must manoeuvre in order to generate observability. It turns out that these manoeuvres are actually necessary but not sufficient to guarantee that the problem becomes observable. In fact, a significant body of the literature pertains to attempting to understand whether this type of problem is solvable. Despite this observability analysis, the following practical questions, which we would like to address in this project, remain challenging: What kind of trajectory should the observer follow to optimize the estimation of the target’s motion? What is the accuracy of that estimate? How to deal with a multitarget environment? How to take into account some physical constraints related to the sonar?
Another aspect of target tracking is to take into account both the uncertainties on the target's measurement and also the signal attenuation due to acoustic propagation. To the best of our knowledge, there are few works focusing on the computation of optimal trajectories of underwater vehicles based on signal attenuation. In this context, we would like address the problem of optimizing the trajectory of the observer to maximize the detection of the acoustic signals issued by the targets. Conversely, given that the targets are also equipped with sonars, how can one optimize the trajectory of the observer itself to keep its own acoustic indiscretion as low as possible with respect to those targets.
It must be emphasized that a human operator can find a suitable trajectory for either of these objectives in the context of a single target. However, if both criteria and/or several targets are taken into account simultaneously, it is hardly possible for a human operator to find such trajectories. From an operational point of view, these questions are therefore of great importance.
Such practical problems are strongly connected to the mathematical topics described in sections 3.2 and 3.3. For example it is clearly related to partially observed stochastic control problems. The algorithmic solutions that we will develop in the framework of submarine passive target tracking will be evaluated on the basis of case studies proposed by Naval Group. Our short-term aim is to obtain explicit results and to develop efficient algorithms to solve the various problems described above.
4.2 Other collaborations
Permanent researchers: M. Chavent, P. Legrand and J. Saracco.
For several years, the team has also had strong collaborations with INRAE which is the French National Research Institute for Agriculture, Food and Environment. More precisely, consumer satisfaction when eating beef is a complex response based on subjective and emotional assessments. Safety and health are very important in addition to taste and convenience but many other parameters are also extremely important for breeders. Many models were recently developed in order to predict each quality trait and to evaluate the possible trade-off that could be accepted in order to satisfy all the operators of the beef chain at the same time. However, in none of these quality prediction systems are issues of joint management of the different expectations addressed. Thus, it is vital to develop a model that integrates the sensory quality of meat but also its nutritional and environmental quality, which are expectations clearly expressed by consumers. Our team are currently developing statistical models and machine learning tools in order to simultaneously manage and optimize the different sets of expectations. Combining dimension reduction methodologies, nonparametric quantiles estimation and “Pareto front’’ approaches could provide an interesting way to address this complex problem. These different aspects are currently in progress.
The team is currently initiating scientific collaboration with the Advanced Data Analytics Group of Sartorius Corporate Research which is an international pharmaceutical and laboratory equipment supplier, covering the segments of Bioprocess Solutions and Lab Products & Services. The current work concerns the development of a partial least squares (PLS) inspired method in the context of multiblocks of covariates (corresponding to different technologies and/or different sampling techniques and statistical procedures) and high dimensional datasets (with the sample size much smaller than the number of variables in the different blocks). The proposed method allows variable selection in the and in the components thanks to interpretable parameters associated with the soft-thresholding of the empirical correlation matrices (between the ’s blocks and the block) decomposed using singular values decomposition (SVD). In addition, the method is able to handle specific missing values (i.e. “missing samples’’ in some covariate blocks). The suggested ddsPLS + Koh Lanta methodology is computationally fast. Some technical and/or theoretical work on this methodology must be naturally pursued in order to further refine this approach. Moreover, another aspect of the future research with Sartorius consists of associating the structures of datasets with the real biological dynamics described, until now, by differential equations and for which the most advanced solutions do not merge with both high dimensional multiblock analysis and these differential equations. Combining these two approaches in a unified framework will certainly have many applications in industry and especially in the biopharmaceutical production.
Within the framework of the GIS ALBATROS, the team has initiated a scientific collaboration with IMS and THALES. The first topic is focused on the measurement of the cognitive load of a pilot through the development of methods for measuring the regularity of biological signals (Hölderian regularity, Detrended Fluctuation Analysis, etc.). The second topic is dedicated to the development of classification techniques of vessels. The different methods we proposed are based on deep learning, evolutionary algorithms and signal processing techniques and are compared to the approaches in the literature.
5 Highlights of the year
- Marie Chavent has been promoted to Full Professor.
- Emma Horton has joined the team as junior INRIA researcher.
6 New software and platforms
6.1 New software
FracLab is a general purpose signal and image processing toolbox based on fractal, multifractal and local regularity methods. FracLab can be approached from two different perspectives: - (multi-) fractal and local regularity analysis: A large number of procedures allow to compute various quantities associated with 1D or 2D signals, such as dimensions, Hölder and 2-microlocal exponents or multifractal spectra.
- Signal/Image processing: Alternatively, one can use FracLab directly to perform many basic tasks in signal processing, including estimation, detection, denoising, modeling, segmentation, classification, and synthesis.
Jacques Lévy Véhel
Antoine Echelard, Christian Choque-Cortez, Jacques Lévy Véhel, Khalid Daoudi, Olivier Barrière, Paulo Goncalves, Pierrick Legrand
Centrale Paris, Mas
Mixed data type arise when observations are described by a mixture of numerical and categorical variables. The R package PCAmixdata extends standard multivariate analysis methods to incorporate this type of data. The key techniques included in the package are PCAmix (PCA of a mixture of numerical and categorical variables), PCArot (rotation in PCAmix) and MFAmix (multiple factor analysis with mixed data within a dataset). The MFAmix procedure handles a mixture of numerical and categorical variables within a group - something which was not possible in the standard MFA procedure. We also included techniques to project new observations onto the principal components of the three methods in the new version of the package.
Clustering, Fair and ethical machine learning
vimpclust is an R package that implements methods related to sparse clustering and variable importance. The package currently allows to perform sparse k-means clustering with a group penalty, so that it automatically selects groups of numerical features. It also allows to perform sparse clustering and variable selection on mixed data (categorical and numerical features), by preprocessing each categorical feature as a group of numerical features. Several methods for visualizing and exploring the results are also provided.
divisive monothetic clustering on dissimilarity matrix
Clustering, Machine learning
The div_diss function implements a divisive monotopic hierarchical classification algorithm.
7 New results
7.1 Statistical modelling
7.1.1 Has breed any effect on beef sensory quality?
A total of 436 young cattle from 15 cattle breeds were reared in as similar conditions as possible to evaluate the impact of breed on sensory quality of beef from longissimus muscle determined by sensory analysis. In 10, two statistical methods for processing the sensory data were compared. The analysis of variance with or without the panelist effect gave similar conclusions indicating that the robustness of the results was not dependent on the method chosen. The 4 meat descriptors (tenderness, juiciness, beef flavor and off-flavor) placed breeds into 5 groups using an unsupervised classification (hierarchical ascending classification). Aberdeen Angus, Highland and Jersey, that have a high lipid content in the muscle studied, differed from the other breeds in that they had a higher beef flavour. The dual-purpose and rustic breeds, Simmental, Casina and Marchigiana, produced significantly less juicy and less tender meat than that from breeds selected for meat production. Overall, despite significant differences previously identified for animal, carcass, muscle and beef traits for the same animals, differences in sensory scores between most of the breeds were small, with only significant differences between the few breeds that had extreme sensory profiles (such as Simmental and Pirenaica).
Authors: A. Conanec; M. D. M. Campo; I. Richardson; P. Ertbjerg; S. Failla, B. Panea; M. Chavent (ASTRAL); J. Saracco (ASTRAL); J. L. Williams; M.-P. Ellies-Oury and J.-F. Hocquette.
7.1.2 Certain relationships between Animal Performance, Sensory Quality and Nutritional Quality can be generalized between various experiments on animal of similar types
In the beef sector, one of the major challenges is to early predict carcass and meat quality and to jointly satisfy the multiple expectations of the various stakeholders. Thus, the objective of this study 13 was to determine if the relationships among carcass, nutritional and sensory qualities established previously by Ellies-Oury et al. (2016) might be generalized to different type of animals.
The Longissimus thoracis muscles of 32 young Charolais bulls were analyzed in terms of sensory and nutritional quality (lipid content and fatty acid composition). These parameters of interest were linked together and to animal performances by using a clustering of variables.
Longissimus thoracis sensory and nutritional qualities appear sometimes antagonistic. Indeed, some “positive” sensory descriptors (juiciness, overall appreciation, overall flavor and overall odor) are negatively related to PUFA proportions. PUFA proportions are positively associated with carcass weight but in the same time with rancid/fish flavors. Moreover, CLA and trans MUFA proportions are positively associated with the “negative” descriptors of greasy feel and residues. To finish, carcass weight and ADG are negatively associated with some “positive” sensory descriptors except tenderness.
It can be concluded from this work that these relationships, that were already established in previous works, are robust between experiments.
In order to highlight robust and generalizable relationships in different contexts, it is now appropriate to apply this method to a larger database containing different traits and various characteristics of breed, slaughter ages, animal types, fattening practices.
Authors: M.-P. Ellies-Oury; D. Durand; A, Listrat; M. Chavent (ASTRAL); J. Saracco (ASTRAL) and D. Gruffat.
7.2 Stochastic control and games
7.2.1 Integro-differential optimality equations for the risk-sensitive control of piecewise deterministic Markov processes
In this paper 12 we study the minimization problem of the infinite-horizon expected exponential utility total cost for continuous-time piecewise deterministic Markov processes with the control acting continuously on the jump intensity and on the transition measure of the process. The action space is supposed to depend on the state variable and the state space is considered to have a frontier such that the process jumps whenever it touches this boundary. We characterize the optimal value function as the minimal solution of an integro-differential optimality equation satisfying some boundary conditions, as well as the existence of a deterministic stationary optimal policy. These results are obtained by using the so-called policy iteration algorithm, under some continuity and compactness assumptions on the parameters of the problem, as well as some non-explosive conditions for the process.
Authors: F. Dufour (ASTRAL) and O. Costa.
7.2.2 On the equivalence of the integral and differential Bellman equations in impulse control problems
When solving optimal impulse control problems, one can use the dynamic programming approach in two different ways: at each time moment, one can make the decision whether to apply a particular type of impulse, leading to the instantaneous change of the state, or apply no impulses at all; or, otherwise, one can plan an impulse after a certain interval, so that the length of that interval is to be optimised along with the type of that impulse. The first method leads to the differential Bellman equation, while the second method leads to the integral Bellman equation. The target of the current article 28 is to prove the equivalence of those Bellman equations in many specific models. Those include abstract dynamical systems, controlled ordinary differential equations, piece-wise deterministic Markov processes and continuous-time Markov decision processes.
Authors: F. Dufour (ASTRAL); A. Piunovskiy and A. Plakhov.
7.2.3 Maximizing the probability of visiting a set infinitely often for a countable state space Markov decision process
In 29, we consider a Markov decision process with countable state space and Borel action space. We are interested in maximizing the probability that the controlled Markov chain visits some subset of the state space infinitely often. We provide sufficient conditions, based on continuity and compactness requirements, together with a stability condition on a parametrized family of auxiliary control models, which imply the existence of an optimal policy that is deterministic and stationary. We compare our hypotheses with those existing in the literature.
Authors: F. Dufour (ASTRAL) and T. Prieto-Rumeau
7.2.4 Stationary Markov Nash equilibria for nonzero-sum constrained ARAT Markov games
In 30, we consider a nonzero-sum Markov game on an abstract measurable state space with compact metric action spaces. The goal of each player is to maximize his respective discounted payoff function under the condition that some constraints on a discounted payoff are satisfied. We are interested in the existence of a Nash or noncooperative equilibrium. Under suitable conditions, which include absolute continuity of the transitions with respect to some reference probability measure, additivity of the payoffs and the transition probabilities (ARAT condition), and continuity in action of the payoff functions and the density function of the transitions of the system, we establish the existence of a constrained stationary Markov Nash equilibrium, that is, the existence of stationary Markov strategies for each of the players yielding an optimal profile within the class of all history-dependent profiles.
Authors: F. Dufour (ASTRAL) and T. Prieto-Rumeau
7.3 Sliced Inverse Regression methods
7.3.1 Advanced topics in Sliced Inverse Regression
Since its introduction in the early 90s, the Sliced Inverse Regression (SIR) methodology has evolved adapting to increasingly complex data sets in contexts combining linear dimension reduction with non linear regression. The assumption of dependence of the response variable with respect to only a few linear combinations of the covariates makes it appealing for many computational and real data application aspects. This work 14 proposes an overview of the most active research directions in SIR modeling from multivariate regression models to regularization and variable selection.
Authors: S. Girard; H. Lorenzo (ASTRAL) and J. Saracco (ASTRAL).
7.3.2 Computational outlier detection methods in sliced inverse regression
Sliced inverse regression (SIR) focuses on the relationship between a dependent variable and a p-dimensional explanatory variable in a semiparametric regression model, in which, the link relies on an index and link function . SIR allows estimating the direction of that forms the effective dimension reduction (EDR) space. Based on the estimated index, the link function can then be nonparametrically estimated using kernel estimator. This two-step approach is sensitive to the presence of outliers in the data. The aim of this paper 22 is to propose computational methods to detect outliers in that kind of single-index regression model. Three outlier detection methods are proposed and their numerical behaviors are illustrated on a simulated sample. To discriminate outliers from "normal" observations, they use IB (in-bags) or OOB (out-of-bags) prediction errors from subsampling or resampling approaches. These methods, implemented in , are compared with each other in a simulation study. An application on a real data is also provided.
Authors: H. Lorenzo (ASTRAL) and J. Saracco (ASTRAL).
7.3.3 Data-Driven Sparse Partial Least Squares
In the supervised high dimensional settings with a large number of variables and a low number of individuals, variable selection allows a simpler interpretation and more reliable predictions. That subspace selection is often managed with supervised tools when the real question is motivated by variable prediction. In 17 we propose a Partial Least Square (PLS) based method, called data-driven sparse PLS (ddsPLS), allowing variable selection both in the covariate and the response parts using a single hyper-parameter per component. The subspace estimation is also performed by tuning a number of underlying parameters. The ddsPLS method is compared to existing methods such as classical PLS and two well established sparse PLS methods through numerical simulations. The observed results are promising both in terms of variable selection and prediction performance. This methodology is based on new prediction quality descriptors associated with the classical R 2 and Q 2 and uses bootstrap sampling to tune parameters and select an optimal regression model.
Authors: H. Lorenzo (ASTRAL); O. Cloarec; R. Thiébaut and J. Saracco (ASTRAL).
7.3.4 Handling Correlations in Random Forests: which Impacts on Variable Importance and Model Interpretability?
The present manuscript 19 tackles the issues of model interpretability and variable importance in random forests, in the presence of correlated input variables. Variable importance criteria based on random permutations are known to be sensitive when input variables are correlated, and may lead for instance to unreliability in the importance ranking. In order to overcome some of the problems raised by correlation, an original variable importance measure is introduced. The proposed measure builds upon an algorithm which clusters the input variables based on their correlations, and summarises each such cluster by a synthetic variable. The effectiveness of the proposed criterion is illustrated through simulations in a regression context, and compared with several existing variable importance measures.
Authors: M. Chavent (ASTRAL); J. Lacaille; A. Mourer and M. Olteanu.
7.4 Positive semigroups and Feynman-Kac formulae
7.4.1 A note on Riccati matrix difference equations
Discrete algebraic Riccati equations and their fixed points are well understood and arise in a variety of applications, however, the time-varying equations have not yet been fully explored in the literature. In this article 24 we provide a self-contained study of discrete time Riccati matrix difference equations. In particular, we provide a novel Riccati semigroup duality formula and a new Floquet-type representation for these equations. Due to the aperiodicity of the underlying flow of the solution matrix, conventional Floquet theory does not apply in this setting and thus further analysis is required. We illustrate the impact of these formulae with an explicit description of the solution of time-varying Riccati difference equations and its fundamental-type solution in terms of the fixed point of the equation and an invertible linear matrix map, as well as uniform upper and lower bounds on the Riccati maps. These are the first results of this type for time varying Riccati matrix difference equations.
Authors: P. Del Moral (ASTRAL) and E. Horton (ASTRAL).
7.4.2 On the Stability of Positive Semigroups
In 26, the stability and contraction properties of positive integral semigroups on locally compact Polish spaces are investigated. We provide a novel analysis based on an extension of V-norm, Dobrushin-type, contraction techniques on functionally weighted Banach spaces for Markov operators. These are applied to a general class of positive and possibly time-inhomogeneous bounded integral semigroups and their normalised versions. Under mild regularity conditions, the Lipschitz-type contraction analysis presented in this article simplifies and extends several exponential estimates developed in the literature. The spectraltype theorems that we develop can also be seen as an extension of Perron-Frobenius and Krein-Rutman theorems for positive operators to time-varying positive semigroups. We review and illustrate in detail the impact of these results in the context of positive semigroups arising in transport theory, physics, mathematical biology and advanced signal processing.
Authors: P. Del Moral (ASTRAL); E. Horton (ASTRAL) and A. Jasra.
7.4.3 Quantum harmonic oscillators and Feynman-Kac path integrals for linear diffusive particles
In 27, we propose a new solvable class of multidimensional quantum harmonic oscillators for a linear diffusive particle and a quadratic energy absorbing well associated with a semi-definite positive matrix force. Under natural and easily checked controllability conditions, the ground state and the zero-point energy are explicitly computed in terms of a positive fixed point of a continuous time algebraic Riccati matrix equation. We also present an explicit solution of normalized and time dependent Feynman-Kac measures in terms of a time varying linear dynamical system coupled with a differential Riccati matrix equation. A refined non asymptotic analysis of the stability of these models is developed based on a recently developed Floquet-type representation of time varying exponential semigroups of Riccati matrices. We provide explicit and non asymptotic estimates of the exponential decays to equilibrium of Feynman-Kac semigroups in terms of Wasserstein distances or Boltzmann-relative entropy. For reversible models we develop a series of functional inequalities including de Bruijn identity, Fisher's information decays, log-Sobolev inequalities, and entropy contraction estimates. In this context, we also provide a complete and explicit description of all the spectrum and the excited states of the Hamiltonian, yielding what seems to be the first result of this type for this class of models. We illustrate these formulae with the traditional harmonic oscillator associated with real time Brownian particles and Mehler's formula. The analysis developed in this article can also be extended to solve time dependent Schrodinger equations equipped with time varying linear diffusions and quadratic potential functions.
Authors: P. Del Moral (ASTRAL) and E. Horton (ASTRAL).
7.5 Branching processes and interacting particle systems
7.5.1 Asymptotic moments of spatial branching processes
Suppose that is either a superprocess or a branching Markov process on a general space E, with non-local branching mechanism and probabilities , when issued from a unit mass at . For a general setting in which the first moment semigroup of displays a Perron-Frobenius type behaviour, we show that 31, for and any positive bounded measurable function on , where the constant can be identified in terms of the principal right eigenfunction and left eigen-measure and is an appropriate determinisitic normalisation, which can be identified explicitly as either polynomial in or exponential in , depending on whether is a critical, supercritical or subcritical process. The method we employ is extremely robust and we are able to extract similarly precise results that additionally give us the moment growth with time of , for bounded measurable on .
Authors: I. Gonzalez; E. Horton (ASTRAL) and A. E. Kyprianou.
7.5.2 Strong laws of large numbers for a growth-fragmentation process with bounded cell sizes
Growth-fragmentation processes model systems of cells that grow continuously over time and then fragment into smaller pieces. Typically, on average, the number of cells in the system exhibits asynchronous exponential growth and, upon compensating for this, the distribution of cell sizes converges to an asymptotic profile. However, the long-term stochastic behaviour of the system is more delicate, and its almost sure asymptotics have been so far largely unexplored. In this article 33 , we study a growth-fragmentation process whose cell sizes are bounded above, and prove the existence of regimes with differing almost sure long-term behaviour.
Authors: E. Horton (ASTRAL) and A. R. Watson.
7.5.3 Stochastic Methods for Neutron Transport Equation III: Generational many-to-one and k-eff
The neutron transport equation (NTE) describes the flux of neutrons over time through an inhomogeneous fissile medium. In the recent articles, [A. M. G. Cox et al., J. Stat. Phys., 176 (2019), pp. 425–455; E. Horton, A. E. Kyprianou, and D. Villemonais, Ann. Appl. Probab., 30 (2020), pp. 2573–2612] a probabilistic solution of the NTE is considered in order to demonstrate a Perron–Frobenius type growth of the solution via its projection onto an associated leading eigenfunction. In [S. C. Harris, E. Horton, and A. E. Kyprianou, Ann. Appl. Probab., 30 (2020), pp. 2815–2845; A. M. G. Cox et al., Monte Carlo Methods for the Neutron Transport Equation, arxiv (2020)], further analysis is performed to understand the implications of this growth both in the stochastic sense as well as from the perspective of Monte Carlo simulation. Such Monte Carlo simulations are prevalent in industrial applications, in particular where regulatory checks are needed in the process of reactor core design. In that setting, however, it turns out that a different notion of growth takes center stage, which is otherwise characterized by another eigenvalue problem. In that setting, the eigenvalue, sometimes called -effective (written ), has the physical interpretation as being the ratio of neutrons produced (during fission events) to the number lost (due to absorption in the reactor or leakage at the boundary) per typical fission event. In this article 11, we aim to supplement [J. Stat. Phys., 176 (2019), pp. 425–455; Ann. Appl. Probab., 30 (2020), pp. 2573–2612; Ann. Appl. Probab., 30 (2020), pp. 2815–2845; Monte Carlo Methods for the Neutron Transport Equation, arxiv (2020)] by developing the stochastic analysis of the NTE further to the setting where a rigorous probabilistic interpretation of is given, both in terms of a Perron–Frobenius type analysis as well as via classical operator analysis. To our knowledge, despite the fact that an extensive engineering literature and industrial Monte Carlo software are concentrated around the estimation of and its associated eigenfunction, we believe that our work is the first rigorous treatment in the probabilistic sense (which underpins some of the aforesaid Monte Carlo simulations).
Authors: A. M. G. Cox; E. Horton (ASTRAL); A. E. Kyprianou and D. Villemonais
Read More: SIAM
7.5.4 Yaglom limit for critical neutron transport
In 32, we consider the classical Yaglom limit theorem for the Neutron Branching Process (NBP) in the setting that the mean semigroup is critical, i.e. its leading eigenvalue is zero. We show that the law of the process conditioned on survival is asymptotically equivalent to an exponential distribution. As part of the proof, we also show that the probability of survival decays inversely proportionally to time. Although Yaglom limit theorems have recently been handled in the setting of spatial branching processes and superprocesses, as well as in the setting of isotropic homogeneous Neutron Branching Processes, our approach and the main novelty of this work is based around a precise result for the scaled asymptotics for the k-th martingale moments of the NBP (rather than the Yaglom limit itself). Our proof of the asymptotic martingale moments turns out to offer a general approach to asymptotic martingale moments of critical branching Markov processes with a non-local branching mechanism. Indeed this is the context in which we give both our moment proofs and the Yaglom limit.
Authors: S. C. Harris; E. Horton (ASTRAL); A. E. Kyprianou and M. Wang.
7.5.5 A theoretical analysis of one-dimensional discrete generation ensemble Kalman particle filters
Despite the widespread usage of discrete generation Ensemble Kalman particle filtering methodology to solve nonlinear and high dimensional filtering and inverse problems, little is known about their mathematical foundations. As genetic-type particle filters (a.k.a. sequential Monte Carlo), this ensemble-type methodology can also be interpreted as mean-field particle approximations of the Kalman-Bucy filtering equation. In contrast with conventional mean-field type interacting particle methods equipped with a globally Lipschitz interacting drift-type function, Ensemble Kalman filters depend on a nonlinear and quadratic-type interaction function defined in terms of the sample covariance of the particles. Most of the literature in applied mathematics and computer science on these sophisticated interacting particle methods amounts to designing different classes of useable observer-type particle methods. These methods are based on a variety of inconsistent but judicious ensemble auxiliary transformations or include additional inflation/localisationtype algorithmic innovations, in order to avoid the inherent time-degeneracy of an insufficient particle ensemble size when solving a filtering problem with an unstable signal. To the best of our knowledge, the first and the only rigorous mathematical analysis of these sophisticated discrete generation particle filters is developed in the pioneering articles by Le Gland-Monbet-Tran and by Mandel-Cobb-Beezley, which were published in the early 2010s. Nevertheless, besides the fact that these studies prove the asymptotic consistency of the Ensemble Kalman filter, they provide exceedingly pessimistic meanerror estimates that grow exponentially fast with respect to the time horizon, even for linear Gaussian filtering problems with stable one dimensional signals. In the present article 25 we develop a novel self-contained and complete stochastic perturbation analysis of the fluctuations, the stability, and the long-time performance of these discrete generation ensemble Kalman particle filters, including time-uniform and non-asymptotic mean-error estimates that apply to possibly unstable signals. To the best of our knowledge, these are the first results of this type in the literature on discrete generation particle filters, including the class of genetic-type particle filters and discrete generation ensemble Kalman filters. The stochastic Riccati difference equations considered in this work are also of interest in their own right, as a prototype of a new class of stochastic rational difference equation.
Authors: P. Del Moral (ASTRAL) and E. Horton (ASTRAL)
7.5.6 Interacting Weighted Ensemble Kalman Filter applied to Underwater Terrain Aided Navigation
Terrain Aided Navigation (TAN) provides a driftfree navigation approach for Unmanned Underwater Vehicles. This paper 21 focuses on an improved version of the Weighted Ensemble Kalman Filter (WEnKF) to solve the TAN problem. We analyze some theoretical limitations of the WEnKF and derive an improved version which ensures that the asymptotic variance of weights remains bounded. This improvement results in an enhanced robustness to nonlinearities in practice. Numerical results are presented and the robustness is demonstrated with respect to conventional WEnKF, yielding twice as less nonconvergence cases.
Authors: C. Palmier (ASTRAL); K. Dahia; N. Merlinge; D. Laneuville (ASTRAL) and P. Del Moral (ASTRAL).
7.6 New developments on detrended fluctuation analysis
7.6.1 Definition of the fluctuation function in the detrendred fluctuation analysis and its variant
The detrended fluctuation analysis (DFA) and its variants are popular methods to analyze the self-similarity of a signal. Two steps characterize them: firstly, the trend of the centered integrated signal is estimated and removed. Secondly, the properties of the so-called fluctuation function which is an approximation of the standard deviation of the resulting process is analyzed. However, it appears that the statistical mean was assumed to be equal to zero to obtain it. As there is no guarantee that this assumption is true a priori, this hypothesis is debatable. The purpose of this paper 9 is to propose two alternative definitions of the fluctuation function. Then, we compare all of them based on a matrix formulation and the filter-based interpretation we recently proposed. This analysis will be useful to show that the approach proposed in the original paper remains a good compromise in terms of accuracy and computational cost.
Authors: B. Berthelot; E. Grivel; P. Legrand (ASTRAL) and A. Giremus.
7.6.2 A matrix approach to get the variance of the square of the fluctuation function of the DFA
The fluctuation analysis (FA) and the detrended fluctuation analysis (DFA) make it possible to estimate the Hurst exponent H, which characterizes the self-similarity of a signal. Both are based on the fact that the so-called fluctuation function, which can be seen as an approximation of the standard deviation of the process scaled in time by multiplying the time variable by a positive constant, depends on H. The main novelty of the paper 15 is to provide the expression of the variance of the square of the fluctuation function, by using a matrix formulation. We show that it depends on the correlation function of the signal under study when it is zero-mean and Gaussian. Illustrations are given when dealing with a zero-mean white Gaussian noise. Moving average processes and first-order autoregressive processes are also addressed.
Authors: E. Grivel; B. Berthelot and P. Legrand (ASTRAL).
7.6.3 DFA-based abacuses providing the Hurst exponent estimate for short-memory processes
The detrended fluctuation analysis (DFA) and its higher-order variant make it possible to estimate the Hurst exponent and therefore to quantify the long-range dependence of a random process. These methods are popular and used in a wide range of applications where they have been proven to be discriminative to characterize or classify processes. Nevertheless, in practice, the signal may be short-memory. In addition, depending on the number of samples available, there is no guarantee that these methods provide the true value of the Hurst exponent, leading the user to draw erroneous conclusions on the long-range dependence of the signal under study. In this paper 16, using a matrix formulation and making no approximation, we first propose to analyze how the DFA and its higher-order variant behave with respect to the number of samples available. Illustrations dealing with short-memory data that can be modeled by a white noise, a moving-average process and a random process whose autocorrelation function exponentially decays are given. Finally, to avoid any wrong conclusions, we propose to derive abacuses linking the value provided by the DFA or its variant with the properties of the signal and the number of samples available.
Authors: E. Grivel; B. Berthelot; P. Legrand (ASTRAL) and A. Giremus
7.6.4 New variants of DFA based on loess and lowess methods: generalization of the detrending moving average
Proposed early in the 90ies, the detrended fluctuation analysis (DFA) can be used to estimate the Hurst exponent and has been proven relevant in various applications, from economics to biomedical. For the last years, variants have been proposed. They differ in the way to estimate the trend of the centered integrated signal. In this paper 18, we recall the main principles of some of these methods, provide explanations on the behaviours of the algorithms and analyze the relevance of new variants based on the Savitzky-Golay filter, also known as the LOESS approach, and the LOWESS. They bridge the gap between the DFA and the detrending moving average (DMA). We hence show that the LOESS-based method is a generalization of the DMA.
Authors: B. Berthelot; E. Grivel and P. Legrand (ASTRAL).
7.7.1 Quina metodologia per despolhar l'enquèsta Bourciez ?
An astonishing team of researchers, teachers and, above all, lovers of the Langue d'Oc with diverse and varied skills, has been carrying out a systematic analysis of the Bourciez survey for just over a year. In 1894, Edouard Bourciez, then a professor at the University of Bordeaux, asked teachers in the Bordeaux and Toulouse academies to translate a reworked version of the parable of the prodigal son into the idiom of the commune where they taught. The results of this investigation undoubtedly exceeded the professor's expectations, since more than 4400 parables were returned to him. These manuscripts are now kept at the University of Bordeaux and form a corpus of 17 volumes of about 1000 manuscript pages each. We are very grateful to the university library for allowing us to have them. For reasons that are not easy to explain, a systematic analysis of this survey has never been carried out, except for the Basque part corresponding to 150 municipalities, i.e. less than 4 percent of the corpus. We are proceeding with the analysis of this survey 20 according to the following four points, not in series, but in parallel: - Computer transcription of the manuscripts ; - Creation of the database; - Data mining and statistical analysis of the database; - Formatting of the results in a format accessible to the greatest number of people.
Author: Alexandre Genadot (ASTRAL)
7.7.2 Hierarchical Agglomerative Clustering under contiguity constraint for Hi-C data analysis
The spatial organisation of the genome within the cell nucleus has a major impact on the regulation of gene expression, with important implications for foetal development, cell differentiation and disease development. This is the initial motivation for this work 23, which aims to study the three-dimensional structure of genetic material and its variations using Hi-C data. First, the modelling of the hierarchical structure of the genome from Hi-C data is investigated. Extensions of a natural statistical tool for examining hierarchical structures, Hierarchical Ascending Classification (HAC), are studied to justify its application to Hi-C. This allows us to justify the modelling of structures by binary trees (derived from HAC). We then develop a method for comparing two samples of trees to be able to identify significant differences.
Authors: N. Randriamihamison (ASTRAL).
8 Bilateral contracts and grants with industry
8.1 Bilateral contracts with industry
Participants: P. Del Moral, F. Dufour, A. Genadot, E. Horton, D. Laneuville, O. Marceau, R. Namyst, A. Nègre, H. Zhang, R. Zhang.
In the applicative domain, an important research focus of the team is the tracking of passive underwater targets in the context of passive underwater acoustic warfare. This is a very complicated practical problem that combines both filtering and stochastic control issues. This research topic is addressed in collaboration with Naval Group. We refer the reader to the section 4.1 for a more detailed description of this theme.
Participants: Benoîte de Saporta, François Dufour, Tiffany Cerchi.
Maintenance, optimization, fleet of industrial equipements. The topic of this collaboration with Université de Montpellier and Thales Optronique is the application of Markov decision processes to the maintenance optimization of a fleet of industrial equipments.
Participants: Bastien Berthelot, Pierrick Legrand.
The collaboration is centered around some contributions to the estimation of the Hurst coefficient and his application on biosignals in the domain of crew monitoring.
Case Law Analytics
Participant: Pierrick Legrand.
Pierrick Legrand is a consultant for the startup Case Law Analytics. The object of the consulting is confidential.
Participants: Hadrien Lorenzo Jérôme Saracco.
The team is currently initiating a scientific collaboration with the Advanced Data Analytics Group of Sartorius Corporate Research which is an international pharmaceutical and laboratory equipment supplier, covering the segments of Bioprocess Solutions and Lab Products and Services. The current work concerns the development of a PLS (Partial Least Squares) inspired method in the context of multiblock of covariates (corresponding to different technologies and/or different sampling, statistical natures...) and high dimensional datasets (with the sample size n much smaller than the number of variables in the different blocks). The proposed method, called ddsPLS for data-driven sparse PLS, allows variable selection in the X and in the Y parts thanks to interpretable parameters associate with the soft-thresolding of the empirical correlation matrices (between the X's blocks and the Y block) decomposed in SVD (Singular Values Decomposition) ways. In addition a methodology to handle specific missing values (i.e. missing samples in some covariate blocks) is also under investigation.
Safran Aircraft Engines
Participants: Marie Chavent, Jérôme Lacaille, Alex Mourer, Madalina Olteanou
The collaboration is centered around an applied mathematics thesis defining a formalism and a methodology for processing and interpretation by the importance of variables (from measurements and calculated indicators) in the case of unsupervised problems. This methodology is accompanied by code programming and a demonstration on an example data set from Safran Aircraft Engines.
8.2 Bilateral Grants with Industry
Participants: Tara Vanhatalo, Pierrick Legrand
Within the framework of Tara Vanhatalo's cifre thesis, a collaboration contract has been signed between Inria, Univiersité de Bordeaux and the company Orosys.
The collaboration is oriented around the modeling of amplifiers by AI.
9 Partnerships and cooperations
9.1 International initiatives
9.1.1 Participation in other International Programs
Emma Horton and her collaborator Ellen Powell (University of Durham, UK) have been awarded a PHC Alliance 2022 grant.
9.2 International research visitors
9.2.1 Visits of international scientists
International visits to the team
Institution of origin:
Context of the visit:
Collaboration with F. Dufour on the topic of game theory
Mobility program/type of mobility:
Institution of origin:
University of Bath
Context of the visit:
Collaboration with E. Horton on the topic of Pólya urns and branching processes
Mobility program/type of mobility:
9.2.2 Visits to international teams
Research stays abroad
University of Amsterdam
Context of the visit:
Collaboration with S. Hautphenne on the topic of parameter estimation in subcritical branching processes. Seminar at the Science Park Informal Probability Meetings.
University of Zurich
Context of the visit:
Collaboration with C. Mailler on the topic of Pólya urns and branching processes.
9.3 National research visitors
9.3.1 Visits of national scientists
Institution of origin:
Paris-Dauphine University/University of Toulouse
Context of the visit:
Collaboration with P. Del Moral and E. Horton on interacting particle systems.
Mobility program/type of mobility:
9.3.2 Visits to national teams
Denis Villemonais and Coralie Fritsch
IECL, University of Lorraine
Context of the visit:
Collaboration with D. Villemonais, C. Fritsch, A. Gégout-Petit and S. Toupance on stochastic models for telomere dynamics.
Mobility program/type of mobility:
9.4 National initiatives
Astral is a joint INRIA team project with Naval Group. The topic of this collaboration is described in section 4.1.
QuAMProcs of the program Project Blanc of the ANR
The mathematical analysis of metastable processes started 75 years ago with the seminal works of Kramers on Fokker-Planck equation. Although the original motivation of Kramers was to « elucidate some points in the theory of the velocity of chemical reactions », it turns out that Kramers’ law is observed to hold in many scientific fields: molecular biology (molecular dynamics), economics (modelization of financial bubbles), climate modeling, etc. Moreover, several widely used efficient numerical methods are justified by the mathematical description of this phenomenon.
Recently, the theory has witnessed some spectacular progress thanks to the insight of new tools coming from Spectral and Partial Differential Equations theory.
Semiclassical methods together with spectral analysis of Witten Laplacian gave very precise results on reversible processes. From a theoretical point of view, the semiclassical approach allowed to prove a complete asymptotic expansion of the small eigen values of Witten Laplacian in various situations (global problems, boundary problems, degenerate diffusions, etc.). The interest in the analysis of boundary problems was rejuvenated by recent works establishing links between the Dirichlet problem on a bounded domain and the analysis of exit event of the domain. These results open numerous perspectives of applications. Recent progress also occurred on the analysis of irreversible processes (e.g. on overdamped Langevin equation in irreversible context or full (inertial) Langevin equation).
The above progresses pave the way for several research tracks motivating our project: overdamped Langevin equations in degenerate situations, general boundary problems in reversible and irreversible case, non-local problems, etc.
Mission pour les initiatives transverses et interdisciplinaires, Défi Modélisation du Vivant, projet MISGIVING
The aim of MISGIVING (MathematIcal Secrets penGuins dIVING) is to use mathematical models to understand the complexity of the multiscale decision process conditioning not only the optimal duration of a dive but also the diving behaviour of a penguin inside a bout. A bout is a sequence of succesive dives where the penguin is chasing prey. The interplay between the chasing period (dives) and the resting period due to the physiological cost of a dive (the time spent at the surface) requires some kind of optimization.
10.1 Promoting scientific activities
10.1.1 Scientific events: organisation
- Pierrick Legrand started the organisation of EA2022 in Exeter, England.
- Pierrick Legrand participated to the organisation of the GIS Albatros Techno Days.
Member of the editorial boards
- J. Saracco is a member of the Editorial Board of Astrostatistics (specialty section of Frontiers in Astronomy and Space Sciences) since 2019.
- Marie Chavent is a member of the editorial committee of the Pratique R collection at EDP Sciences.
- P. Del Moral is an associate editor for the journal Stochastic Analysis and Applications since 2001.
- P. Del Moral is an associate editor for the journal Revista de Matematica: Teoria y aplicaciones since 2009.
- P. Del Moral is an associate editor for the journal Annals of Applied Probability since 2019.
- F. Dufour is corresponding editor of the SIAM Journal of Control and Optimization since 2018.
- F. Dufour is associate editor of the journal Applied Mathematics and Optimization (AMO) since 2018.
- F. Dufour is associate editor of the journal Stochastics: An International Journal of Probability and Stochastic Processes since 2018.
- Pierrick Legrand is the main editor for the Springer LNCS volumes "Artificial Evolution" since 2009.
Reviewer - reviewing activities
- J. Saracco is a regular reviewer for many journal of statistics : The Annals of Statistics, Journal of Multivariate Analysis, Statistica Sinica, Biometrika, Communications in Statistics – Theory and Methods, Computational Statistics and Data Analysis, Journal of Statistical Planning and Inference, Computational Statistics, Journal of Machine Learning Research...
- Marie Chavent has been reviewer for PLOS one, Electronic Journal of Applied Statistical Analysis, Advances in Data Analysis and Classification.
- Alexandre Genadot has been reviewer for Applied Probability journals, Acta Applicandae Mathematicae, IEEE Transactions on Signal Processing, Applied Mathematics and Optimization (AMOP) and Annals of Applied Probability.
- Emma Horton has been reviewer for Annals of Applied Probability, Electronic Journal of Probability, Probability Surveys and the Applied Probability Trust.
- François Dufour has been reviewer for Applied Mathematics and Optimization, IEEE Trans. on Automatic and Control.
- Pierrick Legrand has been reviewer for Digital Signal Processing, Gecco, MDPI.
10.1.3 Invited talks
F. Dufour has been invited to give a talk at the workshop Modern Trends in Controlled Stochastic Processes: Theory and Applications, University of Liverpool, July 2021.
F. Dufour has been invited to give a talk at Symposium on Stochastic Hybrid Systems and Applications, July 2021.
E. Horton was invited to give a mini-course at the 6th Bath-Paris-Beijing Branching Structures Workshop, Septemper 2021.
10.1.4 Leadership within the scientific community
Pierrick Legrand is the president of the association EA.
10.1.5 Scientific expertise
J. Saracco is an elected member of the CNU 26 (National Council of Universities in Applied Mathematics), since 2019.
Emma Horton is a member of the RSS Applied Probability committee.
10.1.6 Research administration
J. Saracco is the leader of the team OptimAl of Institut de Mathématiques de Bordeaux (IMB, UMR CNRS 5251) from 2019.
Marie Chavent is a member appointed to the Council of the Department of Engineering and Digital Sciences (SIN) of the University of Bordeaux.
A. Genadot is member of the Scientific council of the Mathematical Institute of Bordeaux.
A. Genadot is member of the "Commission des emplois de recherche" of Inria BSO.
P. Legrand is member of the CUMI commission.
10.2 Teaching - Supervision - Juries
- J. Saracco is the head of the engineering department of ENSC, Graduate School of Cognitics (applied cognitive science and technology) which is a Bordeaux INP engineering school.
- Marie Chavent is in charge of the first year of the MIASHS degree program at Université de Bordeaux.
- Alexandre Genadot is in charge of the first year of the MIASHS degree program at Université de Bordeaux.
- Pierrick Legrand is in charge of the mathematics program for the MIASHS degree at Université de Bordeaux.
- Licence : P. Legrand, Algèbre, 129h, L1, Université de Bordeaux, France.
- Licence : P. Legrand, Espaces Euclidiens, 46,5h, L2, Université de Bordeaux, France.
- Licence : P. Legrand, Informatique pour les mathématiques, 30h, L2, Université de Bordeaux, France.
- DU : P. Legrand, Evolution Artificielle, Big data, 8h, DU, Bordeaux INP, France.
- Engineer School: Signal processing, 54 hours, ENSC, Bordeaux, France.
- Master: Scientific courses, 10 hours, Université de Bordeaux, France.
- Licence : A. Genadot, Bases en Probabilités, 18h, L1, Université de Bordeaux, France.
- Licence : A. Genadot, Projet Professionnel de l'étudiant, 8h, L1, Université de Bordeaux, France.
- Licence : A. Genadot, Probabilité, 30h, L2, Université de Bordeaux, France.
- Licence : A. Genadot, Techniques d'Enquêtes, 10h, L2, Université de Bordeaux, France.
- Licence : A. Genadot, Modélisation Statistiques, 16.5h, L3, Université de Bordeaux, France.
- Licence : A. Genadot, Préparation Stage, 15h, L3, Université de Bordeaux, France.
- Licence : A. Genadot, TER, 5h, L3, Université de Bordeaux, France.
- Licence : A. Genadot, Processus, 16.5h, L3, Université de Bordeaux, France.
- Licence : A. Genadot, Statistiques, 20h, L3, Bordeaux INP, France.
- Master : A. Genadot, Savoirs Mathématiques, 81h, M1, Université de Bordeaux et ESPE, France.
- Master : A. Genadot, Martingales, 29h, M1, Université de Bordeaux, France.
- Licence : F. Dufour, Probabilités et statistiques, 70h, first year of école ENSEIRB-MATMECA, Institut Polytechnique de Bordeaux, France.
- Master : F. Dufour, Approche probabiliste et méthode de Monte Carlo, 24h, third year of école ENSEIRB-MATMECA, Institut Polytechnique de Bordeaux, France.
- Licence : J. Saracco, Probabilités et Statistique, 27h, first year of Graduate Schools of Engineering ENSC-Bordeaux INP, Institut Polytechnique de Bordeaux, France.
- Licence : J. Saracco, Statistique inférentielle et Analyse des données, 45h, first year of Graduate Schools of Engineering ENSC-Bordeaux INP, Institut Polytechnique de Bordeaux, France.
- Licence : J. Saracco, Statistique pour l’ingénieur, 16h, first year of Graduate Schools of Engineering ENSPIMA-Bordeaux INP, Institut Polytechnique de Bordeaux, France.
- Master : J. Saracco, Modélisation statistique, 81h, second year of Graduate Schools of Engineering ENSC-Bordeaux INP, Institut Polytechnique de Bordeaux, France.
- DU : J. Saracco, Statistique et Big data, 45h, DU BDSI (Big data et statistique pour l'ingénieur), Bordeaux INP, France.
- Licence : M. Chavent, Statistique Inférentielle, 18h, L2, Université de Bordeaux, France
- Licence : M. Chavent, Techniques d'Enquêtes, 10h, L2, Université de Bordeaux, France
- Master : M. Chavent, DataMining, 43h, M2, Université de Bordeaux
- Master : M. Chavent, Machine Learning, 58h, Université de Bordeaux,
- DU: M. Chavent, Apprentissage, 12h, DU BDSI, Bordeaux INP, France
- Licence : E. Horton, Probabilités et Statistiques, 32h, ENSEIRB-MATMECA, Institut Polytechnique de Bordeaux, France.
- Licence : E. Horton, Probabilités, 22h, ENSEIRB-MATMECA, Institut Polytechnique de Bordeaux, France.
- Licence : E. Horton, Probabilités, 24h, ENSEIRB-MATMECA, Institut Polytechnique de Bordeaux, France.
- Licence : E. Horton, 30h, Statistique inférentielle et Analyse des données, first year of Graduate Schools of Engineering ENSC-Bordeaux INP, Institut Polytechnique de Bordeaux, France.
- PhD in progress (2021-2024): John Albechaalany, "Conception d'un modèle multi-objectifs permettant de piloter les liens entre les pratiques d'élevage et la qualité des viandes bovines", supervised by Jérôme Saracco (ASTRAL) and Marie-Pierre Ellies-Oury (Bordeaux Sciences Agro & Inrae).
- PhD in progress (2021-2024): Romain Namyst, "Contrôle optimal stochastique et application à la trajectographie passive", supervised by F. Dufour and A. Genadot. INRIA fellowship. Collaboration with Naval Group.
- PhD in progress: Alex Mourer, "Variables importance in clustering", CIFRE Safran Aircraft Engines, supervised by Jérôme Lacaille (Safran), Madalina Olteanou (SAMM, Paris1), Alex Mourer (doctorant), Marie Chavent (ASTRAL).
- PhD defended in November 2021 (University of Toulouse): Nathanaël Randriamihamison, "Contiguity Constrained Hierarchical Agglomerative Clustering for Hi-C data analysis", supervised by Nathalie Vialaneix (MIAT, INRA Toulouse), Pierre Neuvial (IMT, CNRS), Marie Chavent (ASTRAL).
- PhD defended in November 2021 (University of Bordeaux): Alexandre Conanec, "Modélisation de l'optimisation du pilotage des qualités et des performances de production de la viande bovine", supervised by Marie Chavent(ASTRAL), Jérôme Saracco (ASTRAL), Marie-Pierre Ellies (Bordeaux Sciences Agro & Inrae).
- PhD defended in December 2021: Tiffany Cherchi, “Automated optimal fleet management policy for airborne equipment”, Montpellier University, supervised by B. De Saporta and F. Dufour.
- PhD defended in March 2021: Bastien Berthelot, “Contributions à l'estimation du coefficient de Hurst et son usage sur des biosignaux dans le domaine du crew monitoring”, CIFRE THALES, supervised by P. Legrand.
- PhD defended in December 2021: Camille Palmier, “Nouvelles approches de fusion multi-capteurs par filtrage particulaire pour le recalage de navigation inertielle par corrélation de cartes”, CIFRE, supervised by P. Del Moral, Dann Laneuville (NavalGroup) and Karim Dahia (ONERA).
Pierrick Legrand was member of the PhD jurys of Ulviya Abdulkarimova, Nicolas Scalzitti and Pierre-antoine Chantal.
11 Scientific production
11.1 Major publications
articleStochastic Methods for Neutron Transport Equation III: Generational many-to-one and
.SIAM Journal on Applied Mathematics813May 2021
- 2 miscA theoretical analysis of one-dimensional discrete generation ensemble Kalman particle filters.July 2021
- 3 miscStationary Markov Nash equilibria for nonzero-sum constrained ARAT Markov games.January 2022
- 4 articleCertain relationships between Animal Performance, Sensory Quality and Nutritional Quality can be generalized between various experiments on animal of similar types.Livestock Science250August 2021, 104554
- 5 inproceedings Quina metodologia per despolhar l'enquèsta Bourciez ? Ièr, deman : diga-m'o dins la lenga Montpellier, France November 2021
- 6 articleAdvanced topics in Sliced Inverse Regression.Journal of Multivariate Analysis1882022, 104852
- 7 articleDFA-based abacuses providing the Hurst exponent estimate for short-memory processes.Digital Signal Processing1162021
- 8 articleData-Driven Sparse Partial Least Squares.Statistical Analysis and Data MiningDecember 2021
11.2 Publications of the year
International peer-reviewed conferences
Conferences without proceedings
Scientific book chapters
Doctoral dissertations and habilitation theses
Reports & preprints
11.3 Cited publications
- 34 bookConstrained Markov decision processes.Stochastic ModelingChapman & Hall/CRC, Boca Raton, FL1999, xii+242
- 35 bookMarkov decision processes with applications to finance.UniversitextSpringer, Heidelberg2011, xvi+388URL: https://doi.org/10.1007/978-3-642-18324-9
- 36 bookStochastic optimal control: The discrete time case.139Mathematics in Science and EngineeringNew YorkAcademic Press Inc.1978, xiii+323
- 37 bookContinuous average control of piecewise deterministic Markov processes.SpringerBriefs in MathematicsSpringer, New York2013, xii+116URL: https://doi.org/10.1007/978-1-4614-6983-4
- 38 bookMarkov models and optimization.49Monographs on Statistics and Applied ProbabilityChapman & Hall, London1993, xiv+295URL: http://dx.doi.org/10.1007/978-1-4899-4483-2
- 39 articleSequential Monte Carlo samplers.6832006, 411--436
- 40 bookGenealogical and interacting particle systems with applications.Probability and its ApplicationsSpringer-Verlag, New York2004, 573
- 41 articleOn the stability of Measure Valued Processes with Applications to filtering.1999, 429--434
- 42 articleOn the stability of interacting processes with applications to filtering and genetic algorithms.3722001, 155-194
- 43 articleThe Monte-Carlo method for filtering with discrete-time observations.12032001, 346--368
- 44 bookMean field simulation for Monte Carlo integration.Monographs on Statistics and Applied ProbabilityChapman and Hall2013, URL: http://www.crcpress.com/product/isbn/9781466504059
- 45 bookBranching and Interacting Particle Systems Approximations of Feynman-Kac Formulae with Applications to Non-Linear Filtering.1729Séminaire de Probabilités XXXIVEd. J. Azéma and M. Emery and M. Ledoux and M. Yor, Lecture Notes in Mathematics, Springer-Verlag Berlin2000, 1--145
- 46 articleNon Linear Filtering: Interacting Particle Solution.241996, 555--580
- 47 bookStochastic Processes: From Applications to Theory.Chapman and Hall/CRC2017
- 48 articleOn the stability and the uniform propagation of chaos properties of ensemble Kalman-Bucy filters.2822018, 790--850
- 49 bookControlled Markov processes.235Grundlehren der Mathematischen WissenschaftenBerlinSpringer-Verlag1979, xvii+289
- 50 bookCompetitive Markov decision processes.New YorkSpringer-Verlag1997, xii+393
- 51 bookContinuous-time Markov decision processes.62Stochastic Modelling and Applied ProbabilityTheory and applicationsSpringer-Verlag, Berlin2009, xviii+231URL: https://doi.org/10.1007/978-3-642-02547-1
- 52 bookAdaptive Markov control processes.79Applied Mathematical SciencesNew YorkSpringer-Verlag1989, xiv+148
- 53 bookDiscrete-time Markov control processes: Basic optimality criteria.30Applications of MathematicsNew YorkSpringer-Verlag1996, xiv+216
- 54 bookFurther topics on discrete-time Markov control processes.42Applications of MathematicsNew YorkSpringer-Verlag1999, xiv+276
- 55 bookFoundations of non-stationary dynamic programming with discrete time parameter.Lecture Notes in Operations Research and Mathematical Systems, Vol. 33Springer-Verlag, Berlin-New York1970, vi+160
- 56 articleDiscretization and weak convergence in Markov decision drift processes.Math. Oper. Res.911984, 112--141URL: http://dx.doi.org/10.1287/moor.9.1.112
- 57 articleMarkov decision drift processes: conditions for optimality obtained by discretization.Math. Oper. Res.1011985, 160--173URL: https://doi.org/10.1287/moor.10.1.160
- 58 bookExamples in Markov decision processes.2Imperial College Press Optimization SeriesImperial College Press, London2013, xiv+293
- 59 bookOptimal control of random sequences in problems with constraints.410Mathematics and its ApplicationsWith a preface by V. B. Kolmanovskii and A. N. ShiryaevKluwer Academic Publishers, Dordrecht1997, xii+345URL: https://doi.org/10.1007/978-94-011-5508-3
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