3.1.1 Causal modeling and biases

Participants: Isabelle Guyon, Michèle Sebag, Philippe Caillou, Paola Tubaro

The extraction of causal models, a long goal of AI 143, 113, 144, became a strategic issue as the usage of learned models gradually shifted from prediction to prescription in the last years. This evolution, following Auguste Comte's vision of science (Savoir pour prévoir, afin de pouvoir) indeed reflects the exuberant optimism about AI: Knowledge enables Prediction; Prediction enables Control. However, although predictive models can be based on correlations, prescriptions can only be based on causal models3.

Among the research applications concerned with causal modeling, predictive modeling or collaborative filtering at Tau are all projects described in section 4.1 (see also Section 3.4), studying the relationships between: i) the educational background of persons and the job openings (FUI project JobAgile and DataIA project Vadore); ii) the quality of life at work and the economic performance indicators of the enterprises (ISN Lidex project Amiqap) 115 ; iii) the nutritional items bought by households (at the level of granularity of the barcode) and their health status, as approximated from their body-mass-index (IRS UPSaclay Nutriperso); iv) the actual offer of restaurants and their scores on online rating systems. In these projects, a wealth of data is available (though hardly sufficient for applications ii), iii and iv))) and there is little doubt that these data reflect the imbalances and biases of the world as is, ranging from gender to racial to economical prejudices. Preventing the learned models from perpetuating such biases is essential to deliver an AI endowed with common decency.

In some cases, the bias is known; for instance, the cohorts in the Nutriperso study are more well-off than the average French population, and the Kantar database includes explicit weights to address this bias through importance sampling. In other cases, the bias is only guessed; for instance, the companies for which Secafi data are available hardly correspond to a uniform sample as these data have been gathered upon the request of the company trade union.

Causal relationships are being identified using our recently published paper 18. This work will be continued, as TAU is a partner of the PEPR-IA project Causalit-AI (local PI Michèle Sebag), starting next Spring.

3.1.2 Robustness of Learned Models

Participants: Guillaume Charpiat, Marc Schoenauer, Michèle Sebag

Due to their outstanding performances, deep neural networks and more generally machine learning-based decision making systems, referred to as MLs in the following, have been raising hopes in the recent years to achieve breakthroughs in critical systems, ranging from autonomous vehicles to defense. The main pitfall for such applications lies in the lack of guarantees for MLs robustness.

Specifically, MLs are used when the mainstream software design process does not apply, that is, when no formal specification of the target software behavior is available and/or when the system is embedded in an open unpredictable world. The extensive body of knowledge developed to deliver guarantees about mainstream software $-$ ranging from formal verification, model checking and abstract interpretation to testing, simulation and monitoring $-$ thus does not directly apply either. Another weakness of MLs regards their dependency to the amount and quality of the training data, as their performances are sensitive to slight perturbations of the data distribution. Such perturbations can occur naturally due to domain or concept drift (e.g. due to a change in light intensity or a scratch on a camera lens); they can also result from intentional malicious attacks, a.k.a adversarial examples 159.

These downsides, currently preventing the dissemination of MLs in safety-critical systems (SCS), call for a considerable amount of research, in order to understand when and to which extent an MLs can be certified to provide the desired level of guarantees.

This activity has been put on hold in the team this year, but will be revived in the PEPR-IA SAIF project (starting next Spring) in which TAU is a partner (local PI Guillaume Charpiat).

3.3.1 Auto-*

Participants: Isabelle Guyon, Marc Schoenauer, Michèle Sebag

The so-called Auto-$☆$ task, concerned with selecting a (quasi) optimal algorithm and its hyper-parameters depending on the problem instance at hand, remained a key issue in ML for the last three decades 76, as well as in optimization at large 111, including combinatorial optimization and constraint satisfaction 118 and continuous optimization 73. This issue, tackled by several European projects along the decades, governs the knowledge transfer to industry, due to the shortage of data scientists. It becomes even more crucial as models are more complex and their training requires more computational resources. This has motivated several international challenges devoted to Auto-ML 110 (see also Section 3.4), including the AutoDL challenge series 129 (see also Section 8.6). The latest one AutoGraph aims to bring Automated Machine Learning to Graph Learning, aiming to reduce the human effort and achieve generally top-performing Graph Neural Networks (GNN) 67. The AutoML challenge series results pointed to the importance of meta-learning, which conducted us to pursue a new line of research on meta-learning from learning curves 28, 64 and cross-domain meta-learning 36. The dataset that was developed for ths work was published in the NeurIPS datasets and benchmarks track 39. This line of work led us to explore uses of reinforcement learning as a means to devise policies for meta-learning.

Several approaches have been used to tackle Auto-$☆$ in the literature, and TAU has been particularly active in several of them. Meta-learning aims to build a surrogate performance model, estimating the performance of an algorithm configuration on any problem instance characterized from its meta-feature values 150, 73, 105. Collaborative filtering, considering that a problem instance "likes better" an algorithm configuration yielding a better performance, learns to recommend good algorithms to problem instances 157, 132. Bayesian optimization proceeds by alternatively building a surrogate model of algorithm performances on the problem instance at hand, and tackling it 97. This last approach currently is the prominent one; as shown in 132, the meta-features developed for AutoML are hardly relevant, hampering both meta-learning and collaborative filtering. The design of better features is another long-term research direction, in which TAU has recently been 93, ans still is very active. more recent approach used in TAU 148 extends the Bayesian Optimization approach with a Multi-Armed Bandit algorithm to generate the full Machine Learning pipeline, competing with the famed AutoSKLearn 97 (see Section 8.2.1).

3.3.2 Information theory: adjusting model complexity and data fitting

Participants: Guillaume Charpiat, Marc Schoenauer, Michèle Sebag

In the 60s, Kolmogorov and Solomonoff provided a well-grounded theory for building (probabilistic) models best explaining the available data 151, 107, that is, the shortest programs able to generate these data. Such programs can then be used to generate further data or to answer specific questions (interpreted as missing values in the data). Deep learning, from this viewpoint, efficiently explores a space of computation graphs, described from its hyperparameters (network structure) and parameters (weights). Network training amounts to optimizing these parameters, namely, navigating the space of computational graphs to find a network, as simple as possible, that explain the past observations well.

This vision is at the core of variational auto-encoders 117, directly optimizing a bound on the Kolmogorov complexity of the dataset. More generally variational methods provide quantitative criteria to identify superfluous elements (edges, units) in a neural network, that can potentially be used for structural optimization of the network (Leonard Blier's PhD, started Oct. 2018).

The same principles apply to unsupervised learning, aimed to find the maximum amount of structure hidden in the data, quantified using this information-theoretic criterion.

The known invariances in the data can be exploited to guide the model design (e.g. as translation invariance leads to convolutional structures, or LSTM is shown to enforce the invariance to time affine transformations of the data sequence 160). Scattering transforms exploit similar principles 81. A general theory of how to detect unknown invariances in the data, however, is currently lacking.

The view of information theory and Kolmogorov complexity suggests that key program operations (composition, recursivity, use of predefined routines) should intervene when searching for a good computation graph. One possible framework for exploring the space of computation graphs with such operations is that of Genetic Programming. It is interesting to see that evolutionary computation appeared in the last two years among the best candidates to explore the space of deep learning structures 149, 126. Other approaches might proceed by combining simple models into more powerful ones, e.g. using “Context Tree Weighting” 162 or switch distributions 96. Another option is to formulate neural architecture design as a reinforcement learning problem 74; the value of the building blocks (predefined routines) might be defined using e.g., Monte-Carlo Tree Search. A key difficulty is the computational cost of retraining neural nets from scratch upon modifying their architecture; an option might be to use neutral initializations to support warm-restart.

3.3.3 Analyzing and Learning Complex Systems

Participants: Cyril Furtlehner, Aurélien Decelle, François Landes, Michèle Sebag

Methods and criteria from statistical physics have been widely used in ML. In early days, the capacity of Hopfield networks (associative memories defined by the attractors of an energy function) was investigated by using the replica formalism 70. Restricted Boltzmann machines likewise define a generative model built upon an energy function trained from the data. Along the same lines, Variational Auto-Encoders can be interpreted as systems relating the free energy of the distribution, the information about the data and the entropy (the degree of ignorance about the micro-states of the system) 161. A key promise of the statistical physics perspective and the Bayesian view of deep learning is to harness the tremendous growth of the model size (billions of weights in recent machine translation netwowrks), and make them sustainable through e.g. posterior drop-out 136, weight quantization and probabilistic binary networks 131. Such "informational cooling" of a trained deep network can reduce its size by several orders of magnitude while preserving its performance.

Statistical physics is among the key expertises of Tau, originally only represented by Cyril Furtlehner, later strenghtened by Aurélien Decelle's and François Landes' arrivals in 2014 and 2018. On-going studies are conducted along several directions.

Generative models are most often expressed in terms of a Gibbs distributions $P\left[S\right]=exp(-E[S\left]\right)$, where energy $E$ involves a sum of building blocks, modelling the interactions among variables. This formalization makes it natural to use mean-field methods of statistical physics and associated inference algorithms to both train and exploit such models. The difficulty is to find a good trade-off between the richness of the structure and the efficiency of mean-field approaches. One direction of research pursued in TAU, 99 in the context of traffic forecasting, is to account for the presence of cycles in the interaction graph, to adapt inference algorithms to such graphs with cycles, while constraining graphs to remain compatible with mean-field inference.

Another direction, explored in TAO/TAU in the recent years, is based on the definition and exploitation of self-consistency properties, enforcing principled divide-and-conquer resolutions. In the particular case of the message-passing Affinity Propagation algorithm for instance 165, self-consistency imposes the invariance of the solution when handled at different scales, thus enabling to characterize the critical value of the penalty and other hyper-parameters in closed form (in the case of simple data distributions) or empirically otherwise 100.

A more recent research direction examines the quantity of information in a (deep) neural net along the random matrix theory framework 84. It is addressed in Giancarlo Fissore's PhD, and is detailed in Section 8.2.3.

Finally, we note the recent surge in using ML to address fundamental physics problems: from turbulence to high-energy physics and soft matter (with amorphous materials at its core) 123 or atrophysics/cosmology as well. TAU's dual expertise in Deep Networks and in statistical physics places it in an ideal position to significantly contribute to this domain and shape the methods that will be used by the physics community in the future. In that direction, the PhD thesis of Marion Ullmo and Tony Bonnaire applying statistical method coming either from deep learning or statistical physics to the task of inferring the structure of the cosmic web has show great succes with recents results discussed in Section 8.2.3. François Landes' recent arrival in the team makes TAU a unique place for such interdisciplinary research, thanks to his collaborators from the Simons Collaboration Cracking the Glass Problem (gathering 13 statistical physics teams at the international level). This project is detailed in Section 8.2.3.

Independently, François Landes is actively collaborating with statistical physicists (Alberto Rosso, LPTMS, Univ. Paris-Saclay) and physcists at the frontier with geophysics (Eugenio Lippiello, Second Univ. of Naples) 127, 145. A CNRS grant (80Prime) finances a shared PhD (Vincenzo Schimmenti), at the frontier between seismicity and ML (Alberto Rosso, Marc Schoenauer and François Landes).

7.1.1 Codalab

• Keywords:
  Benchmarking, Competition
• Functional Description:

  Challenges in machine learning and data science are competitions running over several weeks or months to resolve problems using provided datasets or simulated environments. Challenges can be thought of as crowdsourcing, benchmarking, and communication tools. They have been used for decades to test and compare competing solutions in machine learning in a fair and controlled way, to eliminate “inventor-evaluator" bias, and to stimulate the scientific community while promoting reproducible science. See our news: https://codalab.lisn.upsaclay.fr/highlights.

  The new Codalab infrastructure deployed in 2021 includes vast amounts of storage over a distributed Minio (4 physical servers, each with 12 disks of 16 TB) spread over 2 buildings for robustness, and 20 GPU workers in the backend, thanks for the sponsorship of région Ile-de-France, ANR, Université Paris-Saclay, CNRS, INRIA, and ChaLearn, to support 50,000 users, organizing or participating each year to hundreds of competitions.

  Some of the areas in which Codalab is used include Computer vision and medical image analysis, natural language processing, time series prediction, causality, and automatic machine learning. Codalab has been selected by the Région Ile de France to organize industry-scale challenges.

  TAU continues expanding Codalab to accommodate new needs, including teaching. Check recent student projects: https://saclay.chalearn.org/

• News of the Year:

   

  L2RPN The Learning to Run a Power Network competition track in collaboration with RTE France continues. The ICAPS 2021 competition allowed us to go one step further towards making the grid control with reinforcement learning more realistic, allowing adversarial attacks. A new open-source framework Grid2Operate was released.

  AutoDL The Automated Deep Learning (AutoDL) challenge series evolved in the direction of meta learning (https://metalearning.chalearn.org/). We organized a competition fr NwurIPS 2021 sponsored by Google and Microsoft. The results, which will appear in PMLR, indicate that few shot learning (5 shots, 5 classes) is now within reach of the state of the art for small image object recognition, but heavily relies on pre-trained backbone networks, trained on large image datasets.

  Industry challenges The first Ile de France industry challenge was organized on Codalab, in collaboration with Dassault aviation and the results were presented at ICMLA 2021. The goal was to predict sensor data indicating constrains on the fuselage. Surprisingly conventional methods based on ensembles of decision trees dominated this task and outperformed deep learnign methods.

  World use of the platform In 2021, on average, 50 competitions per month were organized on Codalab by researchers from all over the world. Codalab is also used in education to organize code submission homework.

  Codabench December 2021: Codabench (beta) is announced at NeurIPS 2021, see https://www.codabench.org/.

• URL:
  http://­competitions.­codalab.­org
• Contact:
  Isabelle Guyon

7.1.2 Cartolabe

• Name:
  Cartolabe
• Keyword:
  Information visualization
• Functional Description:

  The goal of Cartolabe is to build a visual map representing the scientific activity of an institution/university/domain from published articles and reports. Using the HAL Database, Cartolabe provides the user with a map of the thematics, authors and articles . ML techniques are used for dimensionality reduction, cluster and topics identification, visualisation techniques are used for a scalable 2D representation of the results.

  Cartolabe has in particular been applied to the Grand Debat dataset (3M individual propositions from french Citizen, see https://cartolabe.fr/map/debat). The results were used to test both the scaling capabilities of Cartolabe and its flexibility to non-scientific and non-english corpuses. We also Added sub-map capabilities to display the result of a year/lab/word filtering as an online generated heatmap with only the filtered points to facilitate the exploration. Cartolabe has also been applied in 2020 to the COVID-19 kaggle publication dataset (Cartolabe-COVID project) to explore these publications.

• URL:
  http://­www.­cartolabe.­fr/
• Publication:
  hal-02499006
• Contact:
  Philippe Caillou
• Participants:
  Philippe Caillou, Jean Daniel Fekete, Michèle Sebag, Anne-Catherine Letournel
• Partners:
  LRI - Laboratoire de Recherche en Informatique, CNRS

8.1.1 Causal Modeling

Participants: Philippe Caillou, Isabelle Guyon, Michèle Sebag

PhDs: Armand Lacombe, Cyriaque Rousselot, Nicolas Atienza

Post-doc: Shuyu Dong, Shiyang Yan

Collaboration: Olivier Allais (INRAE); Julia Mink (Univ. Bonn); Jean-Pierre Nadal & Annick Vignes (CAMS, EHESS); David Lopez-Paz (Facebook).

This year, the long awaited journal version of SAM (Structural Agnostic Modelling), has been published in JMLR 18, long after Diviyan Kalainathan's PhD 114. The causal modelling activity continues with three main directions in 2022. The first one is tackled in collaboration with INRAE (Cyriaque Rousselot's PhD), within the Horapest DataIA project. The goal is to assess the causal effects of the diffusion of pesticides in French residential areas, through exploiting the data from the Health Data Hub together with the newly available dataset reporting the concentrations of diverse molecules in 50 stations on a weekly basis (CNEP), and the overall amount of products bought yearly in every postal code (BNVD). The potential effects that will be investigated concern the children' health in the 2019-2022 period, born between 2013 and 2019. The study will contrast the children resident in places with high or low pesticide average concentration on average, and the children with high or low pesticide concentration in utero. Besides getting the data5 the difficulty lies in observational causal modelling from spatio-temporal data with hidden confounders. A second direction is explored in partnership with Fujitsu (Shuyu Dong's postdoc). The goal is to achieve linear Structural Equation Model (SEM)identification from observational data in the large $p$ small $n$ context. The famed characterization of DAG graphs through the exponential trace of the graph proposed by 166 is of cubic complexity in the number $p$ of variables. A low rank decomposition of the inverse covariance matrix combined with an approximation of the gradient has been proposed with a significantly better scalability, at the expense of a moderate loss of accuracy in 25. Our current approach aims to distinguish the statistical and the geometrical errors of SEM identification, respectively related with the estimation of the inverse covariance matrix, and the projection of the associate causal graph on the DAG space 56. A third direction is considered with Nicolas Atienza (PhD Cifre Thales), co-supervised with Johanne Cohen, LISN. The goal is to extend algorithmic recourse 116 to the identification and correction of inappropriate tuning for a critical system. Preliminary investigations have conducted to determining a sufficient and inexpensive characterization of the system state and a patent has been filed on this characterization.

Finally, causality is also at the core of TAU participation in the INRIA Challenge OceanIA, that started in 2021 153. Shiyang Yan's post-doc is dedicated to out-of-distribution learning, motivated by the analysis of the TARA images to identify the ecosystems in the diverse sites of the data collection. The high imbalance of the data among the classes, the prevalence of outliers, are handled using generalized contrastive losses and introducing fake outliers extracted from face images, or created as chimeras.

Other motivating applications for causal modeling are described in section 4.1.

8.1.2 Explainability

Participants: Isabelle Guyon, François Landes, Alessandro Leite, Marc Schoenauer, Michèle Sebag

PhD: Cyriaque Rousselot

Collaboration: MyDataModels; Thales

In Roman Bresson’s PhD 46, (coll. LISN-GALAC, Thales, U. Paderborn; pending patent Bresson-Labreuche-Sebag-Cohen), the goal was to adapt a neuronal architecture to yield an interpretable-by-design model. The extension of this approach is investigated to transform an accurate black-box into a hierarchical choquet integral.

The team is also involved in the proposal for the IPL HyAIAI (Hybrid Approaches for Interpretable AI), coordinated by the LACODAM team (Rennes) dedicated to the design of hybrid approaches that combine state of the art numeric models (e.g., deep neural networks) with explainable symbolic models, in order to be able to integrate high level (domain) constraints in ML models, to give model designers information on ill-performing parts of the model, to provide understandable explanations on its results. On-going collaboration with the Multispeech team in Nancy is concerned with co-supervision of G. Zervakis' PhD (to be defended March 2023), and concerns the use of background knowledge to improve the performances of foundational models in NLP 164 and an analogy based approach for solving target sense verification 34.

An original approach to DNN explainability might arise from the study of structural glasses (8.2.3), with a parallel to Graph Neural Networks (GNNs), that could become an excellent non-trivial example for developing explainability protocols, as we already suggest from results in 66.

Build on collaboration with Raymond Poincaré Hospital, the team is developing tools to increase the interpretability of medical data in applicative context. A first study published in 17 investigates how geometric methods could represent the evolution of patients’ key indicators on a curved manifold to generate meaningful and interpretable representation. These representations could be generalized with minor modifications to temporal data.

Genetic Programming 72 is an Evolutionary Computing technique that evolves models as analytical expressions (Boolean formulae, functions, LISP-like code), that are hopefully easier to understand than black-box NNs with hundreds of thousands of weights. This idea has been picked up by the European FET project TRUST-AI (Transparent, Reliable and Unbiased Smart Tool for AI) that started in October 2020. Alessandro Leite joined the project (and the TAU team) in February 2021 on an ARP position. First work addressed explainable reinforcement learning using GP 33. Current work, recently acepted to EuroGP 2023, concerns the adaptation of the Memetic Semantic Generic Programming 98 to the continuous case. Furthermore, a collaboration with O. Teytaud (Meta), around the follow-up is Mathurin's CIFRE PhD (started Oct. 2022).

8.1.3 Robustness of AI Systems

Participants: Guillaume Charpiat, Marc Schoenauer, Michèle Sebag

PhDs: Roman Bresson

Collaboration: Johanne Cohen (LISN-GALAC) and Christophe Labreuche (Thalès); Eyke Hullermeier (U. Paderborn, Germany).

Though several on-going activitires in this domain have been put on hold (see Section 3.1.2), new research lines have started to emerge, pertaining to robustness.

The first one, already described in section 8.1.2, concerns the indentifiability of the neural net implementing a hierarchical Choquet integral, in the large sample limit.

Another direction, part of A. Lacombe’s on-going PhD, is concerned with privacy. Our primary motivation was to contribute to the understanding of the pandemy, with no former collaboration with hospitals, and therefore, no access to real data. An approach was developed to achieve excessively private learning through a differential-privacy compliant access to the only marginals of the data 122.

We have also explored relationships between theoretical guarantees provided by differential privacy and membership interence attacks 30, as described in Section 8.5.3.

8.2.1 Auto-*

Participants: Guillaume Charpiat, Isabelle Guyon, Marc Schoenauer, Michèle Sebag

PhDs: Léonard Blier, Adrien Pavao, Herilalaina Rakotoarison, Hoazhe Sun, Manon Verbockhaven, Romain Egele

Collaborations: Vincent Renault (SME Artelys); Yann Ollivier (Facebook); Wei-Wei Tu (4Paradigm, Chine); André Elisseeff (Google Zurich); Prasanna Balaprakash (Argonne National labs), among others (for a full list see https://autodl.chalearn.org/ and https://metalearning.chalearn.org/)

Auto-$☆$ studies at Tau investigate several research directions.

After proposing MOSAIC 148, that extends and adapts Monte-Carlo Tree Search to explore the structured space of pre-processing + learning algorithm configurations, and performs on par with AutoSklearn, the winner of Auto-$☆$ international competitions in the last few years, Herilalaina Rakotoarison explored in the end of his PhD the learning of meta-features for tabular data, addressing the lack of expressiveness of the standard Hand-Crafted ones 50. The idea is to use Optimal Transport to align the distribution of the datasets from the training meta-data with that of their best hyperparameter settings in the space of hyperparameter conigurations. The results were presented as a spotlight (top 5% submissions) at ICLR 2022 31.

Heri also contributed to a large benchmarking effort together with Olivier Teytaud, former member of the team, now with Facebook AI Research 19.

In a second direction, with the internship and starting PhD thesis of Manon Verbockhaven, we adopt a functional analysis viewpoint in order to adapt on the fly the architecture of neural networks that are being trained. This allows to start training neural networks with very few neurons and layers, and add them where they are needed, instead of training huge architectures and then pruning them, a common practice in deep learning, for optimization reasons. For this, we quantify the lack of expressivity of a neural network being trained, by analyzing the difference between how the backpropagation would like the activations to change and what the tangent space of the parameters offers as possible activation variations. We can then localize the lacks of expressivity, and add neurons accordingly. It turns out that the optimal weights of the added neurons can be computed in closed form.

A last direction of investigation concerns the design of challenges, that contribute to the collective advance of research in the Auto-$☆$ direction. The team has been very active in the series of AutoML and AutoDL, which has been extended to Meta-Learning, with support from Microsoft, Google, 4Paradigm and ChaLearn, to Meta-Learning, namely meta-learning from learning curves 28, 64 and cross-domain meta-learning 36.

Sef-supervised learning seems to be an avenue with great future, allowing to train reprsentations without costly human labeling. A new challenge accepted as part of the WCCI competition program 2022 is currently running. Another challenge on Neural Architecture Search (NAS) has been run together with a workshop at the CVPR 2021 conference. Preliminary results on NAS have been produced by one of our interns (Romain Egele 95). Further developments have led to effective algorithms to conduct simultaneously NAS and hyper-parameter selection 27, 57. More details on challenges are found in Section 8.6).

8.2.2 Deep Learning: Practical and Theoretical Insights

Participants: Guillaume Charpiat, Isabelle Guyon, Marc Schoenauer, Michèle Sebag

PhDs: Léonard Blier, Zhengying Liu, Adrien Pavao, Haozhe Sun, Romain Egele

Collaboration: Yann Ollivier (Facebook AI Research, Paris)

Although a comprehensive mathematical theory of deep learning is yet to come, theoretical insights from information theory or from dynamical systems can deliver principled improvements to deep learning and/or explain the empirical successes of some architectures compared to others.

During his CIFRE PhD with Facebook AI Research Paris, co-supervised by Yann Ollivier (former TAU member) 45 , Léonard Blier has properly formalized the concepts of successor states and multi-goal functions78, in particular in the case of continuous state spaces. This allowed him to define unbiased algorithms with finite variance to learn such ojects, including the continuous case thanks to approximation functions. In the case of finite environments, new convergence bounds have been obtained for the learning of the value function. These new algorithms capable of learning successor states in turn lead to define and learn new representations for the state space.

The AutoDL challenges, co-organized in TAU (in particular by Isabelle Guyon), also contribute to a better understanding of Deep Learning. It is interesting to note that no Neural Architecture Search algorithm was proposed to solve the different challenges in AutoDL (corresponding to different data types). See section 8.6 for more details.

Our PhD student Haozhe Sun is continuing to work on the problem of modularity in Deep Learning. He wrote a survey under revision and recently submitted a paper on novel algorithms for low-cost AI exploiting modularity. The current trend in Artificial Intelligence (AI) is to heavily rely on systems capable of learning from examples, such as Deep learning (DL) models, a modern embodiment of artificial neural networks. While numerous applications have made it to market in recent years (including self-driving cars, automated assistants, booking services, and chatbots, improvements in search engines, recommendations, and advertising, and heath-care applications, to name a few) DL models are still notoriously hard to deploy in new applications. In particular, the require massive numbers of training examples, hours of GPU training, and highly qualified engineers to hand-tune their architectures. This thesis will contribute to reduce the barrier of entry in using DL models for new applications, a step towards "democratizing AI".

Romain Egele in his PhD in collaboration with Argonne National Labs (USA), is been actively working on Neural Architecture Search (NAS). He developed a package called DeepHyper, allowing users to conduct NAS with genetic algorithms using TensorFlow or PyTorch, the principal Deep Learning frameworks 27. His contributions include applying Recurrent Neural Network Architecture Search for Geophysical Emulation and Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research. In 2022 he published two papers on hyper-parameter optimization. In 57, he proposes to exploit the parallelism in large compute clusters to speed up Bayesian hyper-parameter search in an effective way. In 26 he extends Bayesan optimization algorithms to perform accurate estimations of uncertainties.

8.2.3 Analyzing and Learning Complex Systems

Participants: Cyril Furtlehner, Aurélien Decelle, François Landes, Guillaume Charpiat

PhDs: Giancarlo Fissore, Marion Ullmo

Collaboration: Jacopo Rocchi (LPTMS Paris Sud); the Simons team: Rahul Chako (post-doc), Andrea Liu (UPenn), David Reichman (Columbia), Giulio Biroli (ENS), Olivier Dauchot (ESPCI).; Clément Vignax (EPFL); Yufei Han (Symantec), Nabila Aghanim (Institut d'Astrophysique Spatiale), Tony Bonnaire (ENS Paris).

Generative models constitute an important piece of unsupervised ML techniques which is still under rapid developpment. In this context insights from statistical physics are relevent in particular for energy based models like restricted Boltzmann machines. The information content of a trained restricted Boltzmann machine (RBM) and its learning dynamics can be analyzed precisely with help of ensemble averaging techniques  87, 88. More insight can be obtained by looking at data of low intrinsic dimension, where exact solutions of the RBM can be obtained  89, thanks to a convex relaxation. In particular we have found a 1st order transition mechanisms that may plague the learning in a more advanced part of the learning.In 11 we investigate further this question and show that sampling the equilibrium distribution using the Markov chain Monte Carlo method can be dramatically accelerated when using biased sampling techniques, in particular the Tethered Monte Carlo (TMC) method. This sampling technique can also be used to improve the computation of the log-likelihood gradient during training, leading to dramatic improvements in training RBMs with artificial clustered datasets. On real low-dimensional datasets, this new training method fits RBM models with significantly faster relaxation dynamics than those obtained with standard PCD recipes. Learning dynamics has also been adressed in a different context of feature learning processes in 59 where closed form expressions are obtained for train and test errors via random matrix theory, a characterization of good alignment between the features and the signal and the derivation of a set autonomous equations driving the process at large scale. We have also investigated models of decision-making with approaches grounded in statistical physics, that are able to predict experimental observed data 68. A last point concerns road traffic forecasting, a long standing application of mean-field inference methods based on probabilistic modelling. In 16 we wrap up some of the techniques developed in past works and perform comprehensive experimental tests on various real world Urban traffic dataset, thanks to PTV-SISTeMA, showing the effectiveness of our method.

As mentioned earlier, the use of ML to address fundamental physics problems is quickly growing. In that direction in the context of T. Bonnaire's PhD 80, is undertaken in 12 the first comprehensive and quantitative real-space analysis of the cosmological information content in the environments of the cosmic web (voids, filaments, walls, and nodes) up to non-linear scales, k=0.5 h/Mpc, using a method based on the Gaussian mixture model with a prior forcing the centers to "live" on a tree-graph 13  79, 85. This method has been further developed for handling in particular possible outliers and put into a general formalism  86.

Another place where ML can help address fundamental physics questions is the domain of glasses 40 (how the structure of glasses is related to their dynamics), which is of the major problems in modern theoretical physics (glasses are a key part of the carrer of Giorgio Parisi, a 2021 nobel prize laureate). The idea is to let ML models automatically find the hidden structures (features) that control the flowing or non-flowing state of matter, discriminating liquid from solid states. These models can then help identifying "computational order parameters", that would advance the understanding of physical phenomena 123, 85, on the one hand, and support the development of more complex models, on the other hand. Attacking the problem of amorphous condensed matter by novel Graph Neural Networks (GNN) architectures is a very promising lead, regardless of the precise quantity one may want to predict. Currently GNNs are engineered to deal with molecular systems and/or crystals, but not to deal with amorphous matter. This strategy is currently being investigated by Francesco Pezzicoli (PhD student), who has already demonstrated the generalizing abilities of rotation-equivariant GNNs 66.

8.3.1 Labor Studies

Participants: Philippe Caillou, Isabelle Guyon, Michèle Sebag, Paola Tubaro

PhDs: Guillaume Bied, Armand Lacombe, Assia Wirth

Engineers: Victor Alfonso Naya

Collaboration: Jean-Pierre Nadal (EHESS); Bruno Crépon (ENSAE); Antonio Casilli, Ulrich Laitenberger (Telecom Paris); Odile Chagny (IRES); Francesca Musiani, Mélanie Dulong de Rosnay (CNRS); José Luis Molina (Universitat Autònoma de Barcelona); Antonio Ortega (Universitat de València); Julian Posada (University of Toronto)

A first area of activity of TAU in Computational Social Sciences is the study of labor, from the functioning of the job market, to the rise of new, atypical forms of work in the networked society of internet platforms, and the quality of life at work.

Job markets The DataIA project Vadore (partners ENSAE and Pôle Emploi) benefits from the sustained cooperation and from the wealth of data gathered by Pôle Emploi. The data management is regulated along a 3-partite convention (GENES-ENSAE, Univ Paris-Saclay, Pôle Emploi). Extensive efforts have been required to achieve the data pipelines required to enable learning recommendation models and exploiting them in a confidentiality preserving way (G. Bied's PhD). Primary online testing (beta-test campaigns) have assessed the suitability of the recommendations. A second round of testing at the region Rhone-Alpes scale will take place in 2023.

The learned models are inspected w.r.t. several criteria and requirements. A first criterion regards the robustness of the recommender performances under non-stationary distributions, e.g. due to the Covid pandemy 35. Another criterion concerns the congestion of the job market (share of job offers paid attention to by job seekers). Recommender systems tend to increase the congestion due to the so-called popularity bias. Early attempts to prevent the congestion were investigated in 77, 137, using optimal transport; and this direction will be pursued in S. Nathan's PhD.

A third criterion regards the fairness of the recommendation model. A gender-related gap in several utilities (wages, types of contract, distance-to-job) is observed by contrasting the jobs recommended to men and women, everything else being equal. Most interestingly, this gap parallels the gap among the jobs actually occupied by men and women (everything else being equal). Several directions of research are considered based on this fact, depending on the regulations to be enforced (in French or European public services). The first direction consists in integrating a risk-avoidance sub-model in the recommendation model, to decouple the prejudice effects (from the recruiters' decisions) and the social conditioning (from the job seekers' preferences). The second one aims at defining new population-based and individual-based performance criteria for a job recommender system.

A key difficulty for research on ML-based job recommendation is the lack of open and representative datasets, owing to the very sensitive nature of the data and the protection of vulnerable persons. We have co-organized a workshop (Feb. 2023) gathering researchers and industrials on this topic, in collaboration with Actiris and VDAB (public employment services in Belgium), to identify how this lack of open datasets, hindering the benchmarking of existing systems, can be addressed.

The human labor behind AI

We look at business-to-business platform labour 55 and more specifically at the data "micro-workers" who perform essential, yet marginalized and poorly paid tasks such as labeling objects in a photograph, translating or transcribing short texts, or recording utterances. Micro-workers are recruited through specialist intermediaries across supply chains that span the globe and reproduce inherited North-South outsourcing relationships 43. Further observed inequalities are gender-based 21. Despite the opportunity to telework, the COVID-19 pandemic has adversely affected these workers, widening the gap that separates them from the formally employed 20.

Current work extends this research to workers' skills, competencies and workplace learning practices in an environment in which they support machine learning 22, and to the resilience of these emerging labour markets 44

8.3.2 Health and practices

Participants: Philippe Caillou, Michèle Sebag

PhD: Armand Lacombe, Cyriaque Rousselot

Collaboration: Olivier Allais (INRA); Julia Mink (Univ. Bonn, DE).

Continuing our former partnership with INRAE (in the context of the Initiative de Recherche Stratégique Nutriperso; 103), we proposed the Horapest project to uncover the potential causal relationships between pesticide dissemination and children's health (Cyriaque Rousselot's PhD). The demand of access has been approved by the CNIL and the Health Data Hub; the data are expected in Sept. 2023, and contacts have been taken with the CHU Toulouse for cooperation on complementary data.

8.3.3 Scientific Information System and Visual Querying

Participants: Philippe Caillou, Michèle Sebag

Engineers: Anne-Catherine Letournel, Victor Alfonso Naya

Collaboration: Jean-Daniel Fekete (AVIZ, Inria Saclay)

A third area of activity concerns the 2D visualisation and querying of a corpus of documents. Its initial motivation was related to scientific organisms, institutes or Universities, using their scientific production (set of articles, authors, title, abstract) as corpus. The Cartolabe project (see also Section 7) started as an Inria ADT (coll. Tao and AVIZ, 2015-2017). It received a grant from CNRS (coll. Tau, AVIZ and HCC-LRI, 2018-2019).

The originality of the approach is to rely on the content of the documents (as opposed to, e.g. the graph of co-authoring and citations). This specificity allowed to extend Cartolabe to various corpora, such as Wikipedia, Bibliotheque Nationale de France, or the Software Heritage. Cartolabe was also applied in 2019 to the Grand Debat dataset: to support the interactive exploration of the 3 million propositions; and to check the consistency of the official results of the Grand Debat with the data. Cartolabe has also been applied in 2020 to the COVID-19 kaggle publication dataset (Cartolabe-COVID project) to explore these publications.

Among its intended functionalities are: the visual assessment of a domain and its structuration (who is expert in a scientific domain, how related are the domains); the coverage of an institute expertise relatively to the general expertise; the evolution of domains along time (identification of rising topics). A round of interviews with beta-user scientists has been performed in 2019-2020. Cartolabe usage raises questions at the crossroad of human-centered computing, data visualization and machine learning: i) how to deal with stressed items (the 2D projection of the item similarities poorly reflects their similarities in the high dimensional document space; ii) how to customize the similarity and exploit the users' feedback about relevant neighborhoods. A statement of the current state of the project was published in 2021 82.

8.4.1 Power Grids Management

Participants: Isabelle Guyon, Marc Schoenauer

PhDs: Balthazar Donon, Wenzhuo Liu

Collaboration: Rémi Clément, Patrick Panciatici (RTE)

Our collaboration with RTE, during Benjamin Donnot's (2016-2019) 91 and Balthazar Donon's 47 CIFRE PhDs, is centered on the maintainance of the national French Power Grid. In order to maintain the so-called "(n-1) safety" (see Section 4.2), fast simulations of the electrical flows on the grid are mandatory, that the home-brewed simulator HADES is too slow to provide. The main difficulty of using Deep Neural Networks surrogate models is that the topology of the grid (a graph) should be taken into account, and because all topologies cannot be included in the training set, this requires out-of-sample generalization capabilities of the learned models.

Balthazar Donon developped in his PhD 47 an approach based on Graph Neural Networks (GNNs). From a Power Grid perspective, GNNs can be viewed as including the topology in the heart of the structure of the neural network, and learning some generic transfer function amongst nodes that will perform well on any topology. His work uses a loss that directly aims to minimize Kirshhoff's law on all lines. Theoretical results as well as a generalization of the approach to other optimization problems had been originaly published at NeurIPS 2021 92.

Eva Boguslawski’s CIFRE PhD, that started in Sept. 2022, addresses the problem of global monitoring of the grid through decentralized decision process (aka multi-agent Reinforcement Learning), in the line of the LR2PN challenge (see Section 8.6) that she contributed to organize during a previous internship 32.

8.4.2 Optimization of Local Grids

Participants: Isabelle Guyon, Marc Schoenauer, Michèle Sebag

PhDs: Herilalaina Rakotoarison

Collaboration: Vincent Renault (Artelys).

One of the goals of the ADEME Next project, in collaboration with SME Artelys (see also Section 4.2), is the sizing and capacity design of regional power grids. Though smaller than the national grid, regional and urban grids nevertheless raise scaling issues, in particular because many more fine-grained information must be taken into account for their design and predictive growth.

Regarding the design of such grids, and provided accurate predictions of consumption are available (see below), off-the-shelf graph optimization algorithms can be used. However, they require a careful tuning of their hyperparameters, and this was the motiaton of funding Herilalaina Rakotoarison's PhD, that tackles the automatic tuning of such hyer-parameters (see Section 8.2.1); both the Mosaic algorithm 148 and the Metabu algorithm to learn meta-features are being used for Artelys' home optimizer Knitro, and compared to the state-of-the-art in parameter tuning (confidential deliverable).

8.4.3 Accelerating simulation codes

Participants: Guillaume Charpiat, Marc Schoenauer, Michèle Sebag

PhDs: Matthieu Nastorg

Post-doc: Tamon Nakano

Collaboration: Alessandro Bucci (Safran Tech, former member of the team); Thilbault Faney et Jean-Marc Gratien (IFPEN).

During the 2.5 years that he spent at TAU, funded by the bilateral project with IFPEN, Alessandro Bucci worked on several use case of IFPEN, with the goal of accelerating some softwares that IFPEN uses daily. This IFPEN/TAU collaboration lead to a successful application to a DATAIA program with the ML4CFD project. Direct follow-up of the previous collaboration , a prominent result was obtained on the simulation of diphasic fluid flow in a distillation column: one of the main timeconsuming step in the simulation is the tracking of the interface between the bubbles of the gaz and the liquid they circulate in within the Volume-of-Fluid numerical method: this critical step was replaced with a Graph Neural Network model directly working on the unstrutured mesh, making the industrial application possible 63. ML4CFD is also funding Matthieu Nastorg’s PhD, who significantly accelerated 38 the numerical resolution of the Poisson equation (ubiquitous in CFD, e.g., to compute the pressure in Navier Stokes simulations), based on B. Donon’s Statistical Solvers 92. Note that this resul is more general than its application to energy problems, but was made possible only because of the collaboration with IFPEN.

8.5.1 Space Weather Forecasting

Participants: Cyril Furtlehner, Michèle Sebag

Post-doc: Olivier Bui

Collaboration: Jannis Teunissen (CWI)

Space Weather is broadly defined as the study of the relationships between the variable conditions on the Sun and the space environment surrounding Earth. Aside from its scientific interest from the point of view of fundamental space physics phenomena, Space Weather plays an increasingly important role on our technology-dependent society. In particular, it focuses on events that can affect the performance and reliability of space-borne and ground-based technological systems, such as satellite and electric networks that can be damaged by an enhanced flux of energetic particles interacting with electronic circuits.6

Since 2016, in the context of the Inria-CWI partnership, a collaboration between Tau and the Multiscale Dynamics Group of CWI aims to long-term Space Weather forecasting. The goal is to take advantage of the data produced everyday by satellites surveying the sun and the magnetosphere, and more particularly to relate solar images and the quantities (e.g., electron flux, proton flux, solar wind speed) measured on the L1 libration point between the Earth and the Sun (about 1,500,000 km and 1 hour time forward of Earth). A challenge is to formulate such goals in terms of supervised learning problem, while the "labels" associated to solar images are recorded at L1 (thus with a varying and unknown time lag). In essence, while typical ML models aim to answer the question What, our goal here is to answer both questions What and When. This project has been articulated around Mandar Chandorkar's Phd thesis 83 which has been defended this year in Eindhoven. The continuation of this collaboration is inseured by the hiring of Olivier Bui as a post-doc who's work has consisting in extending preliminary results on solar wind forecasting based on auto-encoded solar magnetograms on a longer period of data corresponding to 2 solar cycles. Negative results have incited us to dig more into physical models of solar wind propagation and try to combine them with ML models in a systematic way.

8.5.2 Genomic Data and Population Genetics

Participants: Guillaume Charpiat, Flora Jay, Aurélien Decelle, Cyril Furtlehner

PhD: Théophile Sanchez, Jérémy Guez

PostDoc: Jean Cury, Burak Yelmen

Collaboration: Bioinfo Team (LISN), Estonian Biocentre (Institute of Genomics, Tartu, Estonia), UNAM (Mexico), U Brown (USA), U Cornell (USA), TIMC-IMAG (Grenoble), MNHN (Paris), Pasteur Institute (Paris)

Thanks to the constant improvement of DNA sequencing technology, large quantities of genetic data should greatly enhance our knowledge about evolution and in particular the past history of a population. This history can be reconstructed over the past thousands of years, by inference from present-day individuals: by comparing their DNA, identifying shared genetic mutations or motifs, their frequency, and their correlations at different genomic scales. Still, the best way to extract information from large genomic data remains an open problem; currently, it mostly relies on drastic dimensionality reduction, considering a few well-studied population genetics features.

For the past decades, simulation-based likelihood-free inference methods have enabled researchers to address numerous population genetics problems. As the richness and amount of simulated and real genetic data keep increasing, the field has a strong opportunity to tackle tasks that current methods hardly solve. However, high data dimensionality forces most methods to summarize large genomic datasets into a relatively small number of handcrafted features (summary statistics).In Theophile Sanchez' PhD 51, we propose an alternative to summary statistics, based on the automatic extraction of relevant information using deep learning techniques. Specifically, we design artificial neural networks (ANNs) that take as input single nucleotide polymorphic sites (SNPs) found in individuals sampled from a single population and infer the past effective population size history. First, we provide guidelines to construct artificial neural networks that comply with the intrinsic properties of SNP data such as invariance to permutation of haplotypes, long scale interactions between SNPs and variable genomic length. Thanks to a Bayesian hyperparameter optimization procedure, we evaluate the performance of multiple networks and compare them to well established methods like Approximate Bayesian Computation (ABC). Even without the expert knowledge of summary statistics, our approach compares fairly well to an ABC based on handcrafted features. Furthermore we show that combining deep learning and ABC can improve performance while taking advantage of both frameworks. Later, we experimented with other types of permutation invariance, based on similar architectures, and achieved a significative performance gain with respect to the state of the art, including w.r.t. ABC on summary statistics (20% gap), which means that we extract information from raw data that is not present in summary statistics. The question is now how to express this information in a human-friendly way.

In the short-term these architectures can be used for demographic inference 60 or selection inference in bacterial populations (ongoing work with a postdoctoral researcher, J Cury, collab: Pasteur Institute, for ancient DNA: UNAM and U Brown); the longer-term goal is to integrate them in various systems handling genetic data or other biological sequence data. Regarding the bacterial populations, we already implemented a flexible simulator that will allow researchers to investigate complex evolutionary scenarios (e.g. dynamics of antibiotic resistance in 2D space through time) with realistic biological processes (bacterial recombination), which was impossible before (collab. U Cornell, MNHN) 14.

In collaboration with the Institute of Genomics of Tartu, we leveraged two types of generative neural networks (Generative Adversarial Networks and Restricted Boltzmann Machines) to learn the high dimensional distributions of real genomic datasets and create artificial genomes 163. These artificial genomes retain important characteristics of the real genomes (genetic allele frequencies and linkage, hidden population structure, ...) without copying them and have the potential to be valuable assets in future genetic studies by providing anonymous substitutes for private databases (such as the ones hold by companies or public institutes like the Institute of Genomics of Tartu. Ongoing work concerns scaling up to the full genome and developing new privacy scores.

We released dnadna, a flexible open-source python-based software for deep learning inference in population genetics7. It is task-agnostic and aims at facilitating the development, reproducibility, dissemination, and reusability of neural networks designed for genetic polymorphism data. dnadna defines multiple user-friendly workflows 152.

8.5.3 Privacy and synthetic data generation

Participants: Isabelle Guyon

PhD: Adrien Pavao

Collaboration: Kristin Bennett and Joe Pedersen (RPI, NY, USA), Wei-Wei Tu (4Paradigm, Chine), Pablo Piantanida (Centrale-Supelec)

While theoretical criteria of privacy preservation, such as “differencial privacy” are important to gain insights into how to protect privacy, they are often impractical, because they put forward pessimistic bounds and impose degrading data and/or model to a point that hampers utility. Additionally, for all practical purposes, data owners seek to obtain guarantees that no privite information is leaked in the form of an empirical statistical test, rather than a more elusive theoretical guarantee. To that end, we have set to work on evaluating the effectiveness of privacy protection agains specific attacks, such as membership inference or attribute inference. We devised an evaluation apparatus called “LTU-attacker” 30, in collaboration with Kristin Bennett, Joe Pedersen, and Wei-Wei Tu and with 2 interns (Rafel Monos-Gomez and Jiangna Huang) have obtained interesting preliminary results demonstrating lack of privacy preservation of most scikit-learn algorithms under membership inference attacks. New directions currently explored in collabotation with Pablo Piantanida include defining a degree of “privacy exposure” of particular individual involving information theoretic arguments.

With Master student Alice Lacan, we have been investigating the modelization of the Covid-19 epidemic propogation using compartimental models, following earlier work by former master student Martin Cepeda. A group of students including Alice entered the "Pandemic response" XPrize and qualified for the final phase. This work was follwed by a paper on estimating uncertainty in time series, in application to prediciting the evolution of the number of Covid cases presented at the BayLearn 2022 conference 121. Alice was invited to give a presentation of this work at the WIDS 2023 conference.

Last but not least regarding Covid-19, F. Landes participated to Inria Saclay collaborative effort to monitor and optimize the emergency bed occupancy in East of France 24.

8.5.4 Sampling molecular conformations

Participants: Guillaume Charpiat

PhD: Loris Felardos

Collaboration: Jérôme Hénin (IBPC), Bruno Raffin (InriAlpes)

Numerical simulations on massively parallel architectures, routinely used to study the dynamics of biomolecules at the atomic scale, produce large amounts of data representing the time trajectories of molecular configurations, with the goal of exploring and sampling all possible configuration basins of given molecules. The configuration space is high-dimensional (10,000+), hindering the use of standard data analytics approaches. The use of advanced data analytics to identify intrinsic configuration patterns could be transformative for the field.

The high-dimensional data produced by molecular simulations live on low-dimensional manifolds; the extraction of these manifolds will enable to drive detailed large-scale simulations further in the configuration space. We study how to bypass simulations by directly predicting, given a molecule formula, its possible configurations. This is done using Graph Neural Networks 58 in a generative way, producing 3D configurations, and constitutes the main part of Loris Felardos’ PhD 48, funded by the Inria Challenge HPC/Big Data. The goal is to sample all possible configurations, and with the right probability. This year we studied various normalizing flow architectures as well as varied training criteria suitable for distributions (Kullback-Leibler divergence in latent or sample space, in one direction or the other one, as it is not symmetric, but also pairwise distances, optimal transport, etc.). It turns out that mode collapse is frequently observed in most cases, even on simple tasks. Further analysis identified several causes for this, from which we built remedies.

8.5.5 Earthquake occurrence prediction

Participants: François Landes, Marc Schoenauer

PhD: Vincenzo Schimmenti

Collaboration: Alberto Rosso (LPTMS)

Earthquakes occur in brittle regions of the Crust typically located at the depth of 5-15 km and characterized by a solid friction, which is at the origin of the stick-slip behaviour. Their magnitude distribution displays thecelebrated Gutenberg-Richter law and a significant increase of the seismic rate is observed after large events (called main shocks). The occurrence of the subsequent earthquakes in the same region, the aftershocks, obeys well established empirical laws that demand to be understood. A change in the seismic rate also happens before a main shock, with an excess of small events compared to the expected rate of aftershocks related to the previous main shock in that region. These additional events are defined as foreshocks of the coming main shock, however they are scarce so that defininig them is a very difficult task. For this reason their statistical fingerprint, so important for human secutiry, remains elusive.

The treatment of rare events by Machine Learning is a challenging yet rapidly evolving domain. At TAU we have a great expertise in data modeling, in particular Bayesian models and Restricted Boltzman Machines (RBMs) have been built to model space weather forecast data (Section 8.5.1). These techniques, inspired from statistical physics, are both based on a probabilistic description of latent variables, allowing the modelling of a large span of data correlations. This kind of models can be extended to study spatially resolved earthquakes, the latent variable here being the local stress within the fault and in the ductile regions. Our goal is to characterize the statistical properties of a sequence of events (foreshocks, main shock and aftershocks) and predict its following history. We will first study the sequences obtained from simulations of the physical model 145. We will answer the following question: given a short sequence of foreshocks, can we predict the future of the sequence? How big will be the main shock? When will it occur? In a second step we will use also the data coming from real sequences, where events are unlabeled. These sequences are public and available (The most accurate catalog is for Southern California, a catalog with 1.81 million earthquakes. It is available at https://scedc.caltech.edu/research-tools/QTMcatalog.html). Concretely, the data consists in the earthquakes’ magnitude, occurrence time and hypocenter locations.

Two parallel directions are being explored, with our PhD Student, Vincenzo Schimmenti:

• The available data can be used to tune the parameters of the new model to improve its accuracy and generalization properties. We will adjust the parameters of the elastic and friction coefficients in order to produce earthquakes with realistic magnitudes. This will allow us to have information about the physical condition in the fault and in the ductile regions.
• We will use our understanding of foreshocks statistics to perform classification of earthquakes with respect to their nature: foreshock, main shock or after shock, and alignment (assignment of the earthquake to a sequence). These labels are known in the synthetic data and unknown in the catalogs, so this would be an instance of semi-supervised learning. Our final goal is real data completion: presented with an incomplete catalog, the machine is asked to complete it with the missing points.

8.5.6 Reduced order model correction

Partecipants: Michele Alessandro Bucci, Marc Schoenauer

PhD: Emmanuel Menier

Collaboration: Mouadh Yagoubi (IRT-SystemX), Lionel Mathelin (DATAFLOT team, LISN)

Numerical simulations of fluid dynamics in industrial applications require the spatial discretization of complex 3D geometries with consequent demanding computational operations for the PDE integration. The computational cost is mitigated by the formulation of Reduced Order Models (ROMs) aiming at describing the flow dynamics in a low dimensional feature space. The Galerkin projection of the driving equations onto a meaningful orthonormal basis speeds up the numerical simulations but introduces numerical errors linked to the underrepresentation of dissipative mechanisms.

Deep Neural Networks can be trained to compensate missing information in the projection basis. By exploiting the projection operation, the ROM correction consists in a forcing term in the reduced dynamical system which has to i) recover the information living in the subspace orthonormal to the projection one ii) ensure that its dynamic is dissipative. A constrained optimization is then employed to minimize the ROM errors but also to ensure the reconstruction and the dissipative nature of the forcing. We tested this solution on benchmarked cases where it is well known that transient dynamics are poorly represented by ROMs. The results 62 show how the correction term improves the prediction while preserving the guarantees of the ROM. Furthermore, the approach was generalized, and the extension was validated on Michelin use case of rubber calendering process 61.

8.5.7 Active Learning for chaotic systems

Participants: Michele Alessandro Bucci (now with SafranTech)

Collaboration: Lionel Mathelin (LISN), Onofrio Semeraro (LISN), Sergio Chibbaro (UPMC), Alexandre Allauzen (ESPCI)

The inference of a data driven model aiming at reproducing chaotic systems is challenging even for the most performing Neural Network architectures. According to the ergodic theory, the amount of data required to converge to the invariant measure of a chaotic system goes exponentially with its intrinsic dimension. Recent work 54 analyzes the amount of data that is sufficient for a priori guaranteeing a faithful model of the physical system.

8.5.8 Graph Neural Networks for Numerical Simulations

Participants: Guillaume Charpiat, Marc Schoenauer, Michèle Sebag

PhDs: Balthazar Donon, Loris Felardos, Wenzhuo Liu, Matthieu Nastorg

Post-doc: Tamon Nakano

Collaboration: Mouahd, Yagoubi (IRT SystemX), Lionel Mathelin (LISN), Alessandro Bucci (Safran Tech, former member of the team); Thilbault Faney et Jean-Marc Gratien (IFPEN).

Many of the works that have been introduced earlier featured the use of Graph Neural Networks to learn how to solve numerical problems invoving data on graphs: Balthazar Donon’s PhD 47 simulated the French Power Grid, Loris Felardos’ PhD 48 learnt the distribution of 3D molecule conformations, Matthieu Nastorg accelerate the numerical resolution of Poisson’s equation on any unstructured mesh with GNNs 38, Tamon Nakano also handled unstructured meshes with GNNs to track the interface between both fluids in multi-phasic flow simulations 63.

But the use of GNNs to approximate the numerical solutions of PDEs on any unstructured mesh, rather than using grid meshes to be able to use the CNNs and the whold zoology of Deep Neural Networks designed for image processing was systematicall studies in Wenzhuo Liu’s PhD: After porting ideas from multi-grid approaches to Finite Elements, and comparing the CNN and GNN approchaes 130, she tackled the poor Out-of-Distribution generalization issue using Meta-Learning 37, improving the OoD learning on CFD simulations of air flow around an airfoil by considering each airfoil shape as a separate task. he is now completing her PhD (defense in March) by applying Transfer Learning to decrease the amount of data to learn accurate simulation on fine meshes using numerous costless simulations on coarse meshes (submitted).

10.1.1 STIC/MATH/CLIMAT AmSud projects

10.1.2 Participation in other International Programs

10.2.1 Horizon Europe

10.2.2 H2020 projects

10.3.1 ANR

• Chaire IA HUMANIA 2020-2024 (600kEuros), Democratizing Artificial Intelligence.

  Coordinator: Isabelle Guyon (TAU)

  Participants: Marc Schoenauer, Michèle Sebag, Anne-Catherine Letournel, François Landes.

• HUSH 2020-2023 (348k euros), HUman Supply cHain behind smart technologies.

  Coordinator : Antonio A. Casilli (Telecom Paris)

  Participants: Paola Tubaro

• SPEED 2021-2024 (49k€) Simulating Physical PDEs Efficiently with Deep Learning

  Coordinator: Lionel Mathelin (LISN (ex-LIMSI))

  Participants: Michele Alessandro Bucci, Guillaume Charpiat, Marc Schoenauer.

• RoDAPoG 2021-2025 (302k€) Robust Deep learning for Artificial genomics and Population Genetics

  Coordinator:Flora Jay,

  Participants: Cyril Furtlehner, Guillaume Charpiat.

10.3.2 Others

• ADEME NEXT 2017-2021, extended 2023 (675 kEuros). Simulation, calibration, and optimization of regional or urban power grids (Section 4.2).

  ADEME (Agence de l'Environnement et de la Maîtrise de l'Energie)

  Coordinator: SME ARTELYS

  Participants Isabelle Guyon, Marc Schoenauer, Michèle Sebag, Victor Berger (PhD), Herilalaina Rakotoarison (PhD), Berna Bakir Batu (Post-doc)

• IPL$$ HPC-BigData 2018-2022 (100 kEuros) High Performance Computing and Big Data (Section 8.5.4)

  Coordinator: Bruno Raffin (Inria Grenoble)

  Participants: Guillaume Charpiat, Loris Felardos (PhD)

• Inria Challenge (formerly IPL) HYAIAI, 2019-2023, HYbrid Approaches for Interpretable Artificial Intelligence

  Coordinator: Elisa Fromont (Lacodam, Inria Rennes)

  Participants: Marc Schoenauer and Michèle Sebag

• Les vraies voix de l'Intelligence Artificielle, 2021-2023 (29k euros), funded by Maison des Sciences de l'Homme Paris-Saclay.

  Coordinator : Paola Tubaro

  Participants: A.A. Casilli (Telecom Paris); I. Vasilescu, L. Lamel, Gilles Adda (CNRS-LISN); J.L. Molina (UAB Barcelona); J.A. Ortega (Univ. València)

• Inria Challenge OceanAI 2021-2025, AI, Data, Models for a Blue Economy

  Coordinator: Nayat Sanchez Pi (Inria Chile)

  Participants: Marc Schoenauer, Michèle Sebag and Shiyang Yan

11.1.1 Scientific events: organisation

11.1.2 Scientific events: selection

11.1.3 Journal

11.1.4 Invited talks

• Guillaume Charpiat, Apprentissage profond pour le recalage, la segmentation et la polygonisation d’images satellitaires, lors du "Séminaire IA: imagerie aérienne et report au plan" de la DGFIP (Bercy) le 29 juin.
• Guillaume Charpiat, invited keynote and co-chair of the session "Trustworthy and Explainable AI" of the day "Franco-German Research and Innovation Network on AI", Rocquencourt, June 14th.
• Flora Jay,
• Marc Schoenauer,
• Michele Sebag, keynote speaker IDA 2022; keynote MIT-France Symposium; keynote French-German symposium;
• Paola Tubaro,
• François Landes, SE(3)-Equivariant GNNs for Machine Learning Glasses, at the CNRS+CFM workshop "Machine Learning Glasses", Paris, November 11, 2022
• Isabelle Guyon, NeurIPS'22 keynote The Data-Centric Era: How ML is Becoming an Experimental Science.

11.1.5 Leadership within the scientific community

• Guillaume Charpiat: creation and co-animation of 2 DigiCosme working groups on the Saclay plateau and beyond: vrAI (verification and robustness of AI) and SNAP (simulations numériques et apprentissage)
• Isabelle Guyon: Member of the board, NeurIPS; Member of the Board, JEDI, Joint European Disruptive Initiative; President and co-founder, ChaLearn, non-for-profit organization dedicated to the organization of challenges.
• Marc Schoenauer: Advisory Board, ACM-SIGEVO, Special Interest Group on Evolutionary Computation; Founding President (since 2015), SPECIES, Society for the Promotion of Evolutionary Computation In Europe and Surroundings, that organizes the yearly series of conferences EvoStar.
• Michèle Sebag: Executive Committee, Institut de Convergence DataIA; Member of IRSN Scientific Council; Member of scientific council of the AMIES Labex;

11.1.6 Scientific expertise

• Guillaume Charpiat: member of the Commission Scientifique (CS) at INRIA Saclay (PhD/post-docs grant allocations)
• Guillaume Charpiat: Jean Zay (GENCI/IDRIS) committee member for resource allocation (GPU) demand expertise
• Guillaume Charpiat: PhD grant allocation: UdopIA jury + DigiCosme Labex jury
• Guillaume Charpiat: jury of "Prix Doctorants STIC du Plateau de Saclay"
• Flora Jay, CR hiring committee, INRAE Toulouse
• Flora Jay, MdC hiring committee, LIX
• Marc Schoenauer, Scientific Advisory Board, BCAM, Bilbao, Spain
• Marc Schoenauer, "Conseil Scientifique", IFPEN
• Marc Schoenauer, "Conseil Scientifique", Mines Paritech
• Marc Schoenauer, "Commission Recherche", Université Paris-Diderot
• Michele Sebag, UDOPIA jury (PhDs)
• Michele Sebag, FNRS (PhDs and Post-docs)
• Michele Sebag, professorship hiring committee, INSA Rouen
• Paola Tubaro, MdC hiring committee, University of Lille
• Paola Tubaro, professorship hiring committee, Sorbonne Université
• Paola Tubaro, associate professorship hiring committee, University of Greenwich (UK)
• Paola Tubaro, assistant professorship hiring committee, University of Insubria (IT)
• Isabelle Guyon, Advisory board Kaggle competitons.

11.1.7 Research administration

• Guillaume Charpiat: head of the Data Science department at LISN, Université Paris-Saclay
• Michele Sebag, elected member of Lab. Council, LISN, Université Paris-Saclay; Member of Council, Institut Pascal
• Paola Tubaro, member of Local Committee of Institut Pascal, Université Paris-Saclay

11.2.1 Teaching

• Licence : Philippe Caillou, Computer Science for students in Accounting and Management, 192h, L1, IUT Sceaux, Univ. Paris Sud.
• Licence : François Landes, Mathematics for Computer Scientists, 51h, L2, Univ. Paris-Sud.
• Licence : François Landes, Introduction to Statistical Learning, 88h, L2, Univ. Paris-Sud.
• Licence : Isabelle Guyon: Introduction to Data Science, L1, Univ. Paris-Sud.
• Licence : Isabelle Guyon, Project: Resolution of mini-challenges (created by M2 students), L2, Univ. Paris-Sud.
• Master : François Landes, A first look inside the ML black box, 25h, M1 Recherche (AI track), U. Paris-Sud.
• Master : François Landes, Machine Learning, 28h, M2 Univ. Paris-sud, physics department (PCS international Master)
• Master : Guillaume Charpiat, Deep Learning in Practice, 21h, M2 Recherche, MVA / Centrale-Supelec / DSBA.
• Master : Guillaume Charpiat, Information Theory, 14h, M1 IA Paris-Sud.
• Master : Guillaume Charpiat, Introduction to Deep Learning, 1h30, Eugloh.
• Master : Guillaume Charpiat, Deep Learning for Physics, 3h, M2 IASD, Paris-Dauphine university
• Master : Isabelle Guyon, Project: Creation of mini-challenges, M2, Univ. Paris-Sud.
• Master : Michèle Sebag, Deep Learning, 4h; Reinforcement Learning, 12h; M2 Recherche, U. Paris-Sud.
• Master : Paola Tubaro, Sociology of social networks, 24h, M2, EHESS/ENS.
• Master : Paola Tubaro, Social and economic network science, 24h, M2, ENSAE.
• Master: Paola Tubaro, Ethics of social and digital data, 12h, Université de Toulouse Jean Jaurès
• Master : Flora Jay, Population genetics inference, 11h, M2, U PSaclay.
• Master : Flora Jay, Machine Learning in Genomics, 6h, M2, PSL. Some Principled Methods for Deep Reinforcement Learning and verification of deep learning: theory and practice, July 23rd.
• Fall school : Flora Jay, Inference using full genome data, 7h, TUM, Germany.

11.2.2 Supervision

• PhD - Leonard BLIER, Some Principled Methods for Deep Reinforcement Learning45, 28/04/22, Yann Ollivier (Facebook AI Research, Paris) and Marc Schoenauer
• PhD - R. BRESSON, Neural learning and validation of hierarchical multi-criteria decision aiding models with interacting criteria46, 2/02/22, Johanne Cohen (Galac, LISN) and Michèle Sebag
• PhD - Balthazar DONON, Deep Statistical Solvers and Power Systems Applications47, 16/03/22, Isabelle Guyon, Marc Schoenauer, and Rémy Clément (RTE)
• PhD - Loris FELARDOS, Neural networks for molecular dynamics simulations48, 2/12/22, Guillaume Charpiat, Jérôme Hénin (IBPC) and Bruno Raffin (InriAlpes)
• PhD - Giancarlo FISSORE, Statistical physics analysis of generative models49, 9/03/22, Aurélien Decelle and Cyril Furtlehner
• PhD - Herilalaina RAKOTOARISON, Automatic Algorithm Configuration for Power Grid Optimization50, 27/06/22, Marc Schoenauer and Michèle Sebag
• PhD - Théophile SANCHEZ, Reconstructing the past: deep learning for population genetics51, 18/03/22, Guillaume Charpiat and Flora Jay
• PhD - Marion ULLMO, Emulation and prediction of cosmic web simulations through deep learning52, from 1/02/22, Nabila Aghanim (Institut d'Astrophysique Spatiale) and Aurélien Decelle
• PhD in progress - Guillaume BIED, Valorisation des Données pour la Recherche d’Emploi, 1/10/2019, Bruno Crepon (CREST-ENSAE) and Philippe Caillou
• PhD in progress - Eva BOGUSLAWSKI Congestion handling on Power Grid governed by complex automata, from 1/05/22, Alessandro Leite, Mathieu Dussartre (RTE) and Marc Schoenauer
• PhD in progress - Romain EGELE, Data-centric automated deep learning, from 1/01/22, Isabelle Guyon/Michèle Sebag
• PhD in progress - Jérémy GUEZ, Statistical inference of cultural transmission of reproductive success, 1/10/2019, Evelyne Heyer (MNHN) and Flora Jay
• PhD in progress - Isabelle HOXHA, Neurocognitive mechanisms of perceptual anticipation in decision-making, from 1/10/2020, Michel-Ange Amorim (Faculté des Sciences du Sport), Sylvain Chevallier and Arnaud Delorme (CerCo)
• PhD in progress - Badr Youbi IDRISSI Learning an invariant representation through continuously evolving data, from 01/10/22, David Lopez-Paz (Meta) and Michèle Sebag
• PhD in progress - Armand LACOMBE, Causal Modeling for Vocational training Recommendation, 1/10/2019, Michele Sebag and Philippe Caillou
• PhD in progress - Wenzhuo LIU, Graph Neural Networks for Numerical Simulation of PDEs, from 1/11/2019, Mouadh Yagoubi (IRT SystemX) and Marc Schoenauer
• PhD in progress - Romain LLORIA. Geometrical Robust Blind Source Separation: Application to EEG classification, from 1/11/2022, Frédéric Pascal (L2S), Florent Bouchard (L2S), and Sylvain Chevallier
• PhD in progress - Emmanuel MENIER, Complementary Deep Reduced Order Model, from 1/9/2020, Michele Alessandro Bucci and Marc Schoenauer
• PhD in progress - Thibault MONSEL, Active Deep Learning for Complex Physical Systems, 1/12/21, Alexandre Allauzen (LAMSADE), Guillaume Charpiat, Lionel Mathelin (LISN), Onofrio Semeraro (LISN)
• PhD in progress - Mathieu NASTORG, Machine Learning enhanced resolution of Navier-Stokes equations on general unstructured grids, 4/1/2021, Guillaume Charpiat and Michele Alessandro Bucci.
• PhD in progress - Adrien PAVAO, Theory and practice of challenge organization, from 1/03/2020, Isabelle Guyon.
• PhD in progress - Francisco PEZZICOLI A new generation of Graph Neural Networks to tackle amorphous materials from 1/11/2021, François Landes and Guillaume Charpiat.
• PhD in progress - Audrey POINSOT, Causal Uncertainty Quantification under Partial Knowledge and Low Data Regimes, from 1/03/22, Nicolas Chesneau (Ekimetrics), Guillaume Charpiat, Alessandro Leite, and Marc Schoenauer
• PhD in progress - Arnaud QUELIN, Infering Human population history with approximated Bayesian computation and machine learning, from ancient and recent genoms' polymorphism data, from 1/10/22, Frédéric Austerlitz (MNHN), Flora Jay
• PhD in progress - Cyriaque ROUSSELOT, Spatio-temporal causal discovery – Application to modeling pesticides impact, from 1/10/22, Michèle Sebag
• PhD in progress - Vincenzo SCHIMMENTI, Eartquake Predictions: Machine Learned Features using Expert Models Simulations, from 1/11/2020, François Landes and Alberto Rosso (LPTMS)
• PhD in progress - Antoine SZATKOWNIK, Deep learning for population genetics, from 1/10/22, Flora Jay, Burak Yelmen, Cyril Furtlehner and Guillaume Charpiat
• PhD in progress - Manon VERBOCKHAVEN, Spotting and fixing expressivity bottlenecks, from 11/2021, Sylvain Chevallier and Guillaume Charpiat
• PhD in progress - Assia WIRTH, Coloniality of the production of facial recognition technologies, started 01/04/2021, Paola Tubaro
• PhD in progress - Maria Sayu YAMAMOTO, Tackling the large variability of EEG data using Riemannian geometry toward reliable Brain-Computer Interfaces, from 01-04-2021, Sylvain Chevallier and Fabien Lotte (INRIA Bordeaux Potioc)

11.2.3 Juries

• Marc Schoenauer, PhD jury member, Paul Defossé, 22/12/22, CMAP Ecole Polytechnique, Palaiseau; PhD jury member, Ekhi Ajuria, 5/12/2022, Cerfacs, Toulouse; PhD Committee (2nd year), Kaitlin Maile, 16/12/2022, IRIT, Toulouse.
• Guillaume Charpiat, PhD jury member, Arthur Ouaknine, 04/03/22, Telecom, IPP, Palaiseau
• Michèle Sebag: HdR jury member: Ievgen Redko; Thomas Lampert; Raphael Féraud; PhD jury member: Teo Sanchez; Naoufal Acharki; Marwa Kechaou; Fatoumata Dama

11.3.1 Internal or external Inria responsibilities

• Marc Schoenauer, Deputy Research Director in charge of AI
• Marc Schoenauer, sherpa for Inria as pilot institution of the PEPR-IA (together with CEA and CNRS)
• Marc Schoenauer, scientific coordinator of ICT49 CSA Adra-e (coordinated by Inria)

11.3.2 Interventions

• Michèle Sebag; France Culture; RadioLibertaire; Exposé invité Sinclair Lab; Exposé invité, Labex Nano-Saclay; participation table ronde, Expertise sur la régulation numérique, ministère des Finances.

International journals

• 11 articleN.Nicolas Béreux, A.Aurélien Decelle, C.Cyril Furtlehner and B.Beatriz Seoane. Learning a Restricted Boltzmann Machine using biased Monte Carlo sampling.SciPost Physics2022
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• 12 articleT.Tony Bonnaire, N.Nabila Aghanim, J.Joseph Kuruvilla and A.Aurélien Decelle. Cosmology with cosmic web environments I. Real-space power spectra.Astronomy and Astrophysics - A&A661A146May 2022
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• 13 articleT.Tony Bonnaire, N.Nabila Aghanim, J.Joseph Kuruvilla and A.Aurélien Decelle. Cosmology with cosmic web environments: I. Real-space power spectra.Astronomy and Astrophysics - A&A661May 2022, A146
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• 14 articleJ.Jean Cury, B. C.Benjamin C Haller, G.Guillaume Achaz and F.Flora Jay. Simulation of bacterial populations with SLiM.Peer Community JournalJanuary 2022
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• 15 articleA.Aurélien Decelle. Fundamental problems in Statistical Physics XV: Lecture on Machine Learning.Physica A: Statistical Mechanics and its ApplicationsSeptember 2022, 128154
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• 16 articleC.Cyril Furtlehner, J.-M.Jean-Marc Lasgouttes, A.Alessandro Attanasi, M.Marco Pezzulla and G.Guido Gentile. Short-term Forecasting of Urban Traffic using Spatio-Temporal Markov Field.IEEE Transactions on Intelligent Transportation Systems2382022, 10858-10867
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• 17 articleS.Sarah Hartley, G.Guillaume Bao, M.Marine Zagdoun, S.Sylvain Chevallier, F.Frédéric Lofaso, A.Antoine Leotard and E.Eric Azabou. Noninvasive Vagus Nerve Stimulation: A New Therapeutic Approach for Pharmacoresistant Restless Legs Syndrome.NeuromodulationNovember 2022
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• 18 articleD.Diviyan Kalainathan, O.Olivier Goudet, I.Isabelle Guyon, D.David Lopez-Paz and M.Michèle Sebag. Structural Agnostic Modeling: Adversarial Learning of Causal Graphs.Journal of Machine Learning Research2022
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• 19 articleL.Laurent Meunier, H.Herilalaina Rakotoarison, P. K.Pak Kan Wong, B.Baptiste Roziere, J.Jeremy Rapin, O.Olivier Teytaud, A.Antoine Moreau and C.Carola Doerr. Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking.IEEE Transactions on Evolutionary Computation2632022
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• 20 articleP.Paola Tubaro and A. A.Antonio A Casilli. Who bears the burden of a pandemic? COVID-19 and the transfer of risk to digital platform workers.American Behavioral ScientistJanuary 2022
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• 21 articleP.Paola Tubaro, M.Marion Coville, C.Clément Le Ludec and A. A.Antonio A Casilli. Hidden inequalities: the gendered labour of women on micro-tasking platforms.Internet Policy Review1112022
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• 22 articleP.Paola Tubaro. Learners in the loop: hidden human skills in machine intelligence.Sociologia del Lavoro1632022, 110-129
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• 23 articleZ.Zhen Xu, S.Sergio Escalera, A.Adrien Pavao, M.Magali Richard, W.-W.Wei-Wei Tu, Q.Quanming Yao, H.Huan Zhao and I.Isabelle Guyon. Codabench: Flexible, Easy-to-Use and Reproducible Meta-Benchmark Platform.PatternsJuly 2022
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National journals

• 24 articleC.Consortium Icubam, L.Laurent Bonnasse-Gahot, M.Maxime Dénès, G.Gabriel Dulac-Arnold, S.Sertan Girgin, F.François Husson, V.Valentin Iovene, J.Julie Josse, A.Antoine Kimmoun, F.François Landes, J.-P.Jean-Pierre Nadal, R.Romain Primet, F.Frederico Quintao, P. G.Pierre Guillaume Raverdy, V.Vincent Rouvreau, O.Olivier Teboul and R.Roman Yurchak. ICU Bed Availability Monitoring and analysis in the Grand Est region of France during the COVID-19 epidemic.Statistique et SociétéMarch 2022
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International peer-reviewed conferences

• 25 inproceedingsS.Shuyu Dong and M.Michèle Sebag. From graphs to DAGs: a low-complexity model and a scalable algorithm.ECML-PKDD 2022 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in DatabasesGrenoble, FranceSeptember 2022
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• 26 inproceedingsM.Matthieu Dorier, R.Romain Egele, P.Prasanna Balaprakash, J.Jaehoon Koo, S.Sandeep Madireddy, S.Srinivasan Ramesh, A.Allen Malony and R.Rob Ross. HPC Storage Service Autotuning Using Variational- Autoencoder -Guided Asynchronous Bayesian Optimization.CLUSTER 2022 - IEEE International Conference on Cluster Computing (CLUSTER)Heidelberg, GermanyIEEESeptember 2022, 381-393
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• 27 inproceedingsR.Romain Egele, R.Romit Maulik, K.Krishnan Raghavan, B.Bethany Lusch, I.Isabelle Guyon and P.Prasanna Balaprakash. AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification.26TH International Conference on Pattern RecognitionMontréal, CanadaIEEEAugust 2022, 1908-1914
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• 28 inproceedingsM. H.Manh Hung Nguyen, L.Lisheng Sun-Hosoya, N.Nathan Grinsztajn and I.Isabelle Guyon. Meta-learning from Learning Curves: Challenge Design and Baseline Results.IJCNN 2022 - International Joint Conference on Neural NetworksPadua, ItalyIEEEJuly 2022, 1-8
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• 29 inproceedingsA.Adrien Pavao, Z.Zhengying Liu and I.Isabelle Guyon. Filtering participants improves generalization in competitions and benchmarks.ESANN 2022 - European Symposium on Artificial Neural NetworksBruges, BelgiumOctober 2022
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• 30 inproceedingsJ.Joseph Pedersen, R.Rafael Muñoz Gómez, J.Jiangnan Huang, H.Haozhe Sun, W.-W.Wei-Wei Tu and I.Isabelle Guyon. LTU Attacker for Membership Inference.Third AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-22)virtual, France2022
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• 31 inproceedingsH.Herilalaina Rakotoarison, L.Louisot Milijaona, A.Andry Rasoanaivo, M.Michèle Sebag and M.Marc Schoenauer. Learning Meta-features for AutoML.ICLR 2022 - International Conference on Learning Representations (spotlight)Virtual, United StatesApril 2022
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• 32 inproceedingsG.Gaëtan Serré, E.Eva Boguslawski, B.Benjamin Donnot, A.Adrien Pavão, I.Isabelle Guyon and A.Antoine Marot. Reinforcement learning for Energies of the future and carbon neutrality: a Challenge Design.SSCI 2022 - IEEE Symposium Series on Computational IntelligenceSingapour, SingaporeDecember 2022
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• 33 inproceedingsM.Mathurin Videau, A.Alessandro Leite, O.Olivier Teytaud and M.Marc Schoenauer. Multi-Objective Genetic Programming for Explainable Reinforcement Learning.Proc. EuroGP 202213223Lecture Notes in Computer ScienceMadrid, FranceSpringer International PublishingApril 2022, 278-293
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• 34 inproceedingsG.Georgios Zervakis, E.Emmanuel Vincent, M.Miguel Couceiro, M.Marc Schoenauer and E.Esteban Marquer. An analogy based approach for solving target sense verification.NLPIR 2022 - 6th International Conference on Natural Language Processing and Information RetrievalBangkok, ThailandDecember 2022
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Conferences without proceedings

• 35 inproceedingsG.Guillaume Bied, S.Solal Nathan, E.Elia Pérennes, V. A.Victor Alfonso Naya, P.Philippe Caillou, B.Bruno Crépon, C.Christophe Gaillac and M.Michèle Sebag. Recommender system in a non-stationary context: recommending job ads in pandemic times.FEAST workshop ECML-PKDD 2022Grenoble, FranceSeptember 2022
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• 36 inproceedingsD.Dustin Carrión-Ojeda, H.Hong Chen, A.Adrian El Baz, S.Sergio Escalera, C.Chaoyu Guan, I.Isabelle Guyon, I.Ihsan Ullah, X.Xin Wang and W.Wenwu Zhu. NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results.Meta-Knowledge Transfer/Communication in Different SystemsGrenoble, FranceSeptember 2022
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• 37 inproceedingsW.Wenzhuo Liu, M.Mouadh Yagoubi, D.David Danan and M.Marc Schoenauer. Multi-Fidelity Transfer Learning for accurate data-based PDE approximation.NeurIPS 2022 - Workshop on Machine Learning and the Physical SciencesNew Orleans, United StatesNovember 2022
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• 38 inproceedingsM.Matthieu Nastorg, M.Marc Schoenauer, G.Guillaume Charpiat, T.Thibault Faney, J.-M.Jean-Marc Gratien and M. A.Michele Alessandro Bucci. DS-GPS : A Deep Statistical Graph Poisson Solver.NeurIPS 2022 - Machine Learning and the Physical Sciences, workshopNew-Orleans, United StatesDecember 2022
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• 39 inproceedingsI.Ihsan Ullah, D.Dustin Carrión-Ojeda, S.Sergio Escalera, I.Isabelle Guyon, M.Mike Huisman, F.Felix Mohr, J. N.Jan N van Rijn, H.Haozhe Sun, J.Joaquin Vanschoren and P. A.Phan Anh Vu. Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification.36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks.New Orleans, United States2022
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Scientific book chapters

• 40 inbookF.Francesco Arceri, F. P.François P. Landes, L.Ludovic Berthier and G.Giulio Biroli. Glasses and aging: A Statistical Mechanics Perspective.Encyclopedia of Complexity and Systems Science (Living Reference)March 2022
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• 41 inbookE.Erik Aurell, J.Jean Barbier, A.Aurélien Decelle and R.Roberto Mulet. The mighty force: statistical inference and high-dimensional statistics.Spin Glass Theory & Far Beyond - Replica Symmetry Breaking after 40 YearsWorld Scientific2022
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• 42 inbookA. A.Adrian Alan Pol, G.Gianluca Cerminara, C.Cécile Germain and M.Maurizio Pierini. Data Quality Monitoring Anomaly Detection.Artificial Intelligence for High Energy PhysicsWorld ScientificMarch 2022
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• 43 inbookP.Paola Tubaro and A. A.Antonio A Casilli. Human Listeners and Virtual Assistants: Privacy and Labor Arbitrage in the Production of Smart Technologies.Digital Work in the Planetary MarketThe MIT Press2022
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• 44 inbookP.Paola Tubaro. Social networks and resilience in emerging labor markets.A Research Agenda for Social Networks and Social ResilienceEdward ElgarNovember 2022, 45-57
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Doctoral dissertations and habilitation theses

• 45 thesisL.Léonard Blier. Some Principled Methods for Deep Reinforcement Learning.Université Paris-SaclayApril 2022
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• 46 thesisR.Roman Bresson. Neural learning and validation of hierarchical multi-criteria decision aiding models with interacting criteria.Université Paris-SaclayFebruary 2022
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• 47 thesisB.Balthazar Donon. Deep statistical solvers & power systems applications.Université Paris-SaclayMarch 2022
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• 48 thesisL.Loris Felardos. Data-free Generation of Molecular Configurations with Normalizing Flows.Université Grenoble-AlpesDecember 2022
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• 49 thesisG.Giancarlo Fissore. Generative modeling : statistical physics of Restricted Boltzmann Machines, learning with missing information and scalable training of Linear Flows.Université Paris-SaclayMarch 2022
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• 50 thesisH.Herilalaina Rakotoarison. Some contributions to AutoML : hyper-parameter optimization and meta-learning.Université Paris-SaclayJune 2022
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• 51 thesisT.Théophile Sanchez. Reconstructing our past ˸ deep learning for population genetics.Université Paris-SaclayMarch 2022
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• 52 thesisM.Marion Ullmo. Emulation and prediction of cosmic web simulations through deep learning.Université Paris-SaclayFebruary 2022
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Reports & preprints

• 53 miscA.Adriano Barra, G.Giovanni Catania, A.Aurélien Decelle and B.Beatriz Seoane. Thermodynamics of bidirectional associative memories.November 2022
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• 54 miscM. A.Michele Alessandro Bucci, O.Onofrio Semeraro, A.Alexandre Allauzen, S.Sergio Chibbaro and L.Lionel Mathelin. Leveraging the structure of dynamical systems for data-driven modeling.November 2022
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• 55 reportO.Odile Chagny, A. A.Antonio A. Casilli, D.Diouldé Chartier, T.Tristan d'Avezac, F.Fred Pailler, P.Perrine Rothey, P.Paola Tubaro, M.Maxime Cornet, M.Mandie Joulin and S.Salif Kagambega. Les Nouveaux Intermédiaires du Travail B2B: Comparer les modèles d'affaires dans l'économie numérique collaborative.27DARES - Direction de l'animation de la recherche, des études et des statistiques du Ministère du travail, de l'emploi et de l'insertionMarch 2022
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• 56 miscS.Shuyu Dong, K.Kento Uemura, A.Akito Fujii, S.Shuang Chang, Y.Yusuke Koyanagi, K.Koji Maruhashi and M.Michèle Sebag. High-Dimensional Causal Discovery: Learning from Inverse Covariance via Independence-based Decomposition.December 2022
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• 57 miscR.Romain Egele, J.Joceran Gouneau, V.Venkatram Vishwanath, I.Isabelle Guyon and P.Prasanna Balaprakash. Asynchronous Distributed Bayesian Optimization at HPC Scale.November 2022
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• 58 miscL.Loris Felardos, J.Jérôme Hénin and G.Guillaume Charpiat. Designing losses for data-free training of normalizing flows on Boltzmann distributions.January 2023
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• 59 miscC.Cyril Furtlehner. Free Dynamics of Feature Learning Processes.October 2022
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• 60 miscJ.Jérémy Guez, G.Guillaume Achaz, F.François Bienvenu, J.Jean Cury, B.Bruno Toupance, É.Évelyne Heyer, F.Flora Jay and F.Frédéric Austerlitz. Cultural transmission of reproductive success impacts genomic diversity, coalescent tree topologies and demographic inferences.2022
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• 61 miscE.Emmanuel Menier, M. A.Michele Alessandro Bucci, M.Mouadh Yagoubi, L.Lionel Mathelin, T.Thibault Dairay, R.Raphael Meunier and M.Marc Schoenauer. Continuous Methods : Adaptively intrusive reduced order model closure.July 2022
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• 62 miscE.Emmanuel Menier, M. A.Michele Alessandro Bucci, M.Mouadh Yagoubi, L.Lionel Mathelin and M.Marc Schoenauer. CD-ROM: Complementary Deep-Reduced Order Model.November 2022
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• 63 miscT.Tamon Nakano, M. A.Michele Alessandro Bucci, J.-M.Jean-Marc Gratien, T.Thibault Faney and G.Guillaume Charpiat. Machine learning model for gas-liquid interface reconstruction in CFD numerical simulations.July 2022
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• 64 miscM. H.Manh Hung Nguyen, L.Lisheng Sun, N.Nathan Grinsztajn and I.Isabelle Guyon. Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round.August 2022
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• 65 reportA.Adrien Pavao, I.Isabelle Guyon, A.-C.Anne-Catherine Letournel, X.Xavier Baró, H.Hugo Escalante, S.Sergio Escalera, T.Tyler Thomas and Z.Zhen Xu. CodaLab Competitions: An open source platform to organize scientific challenges.Université Paris-Saclay, FRA.April 2022
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• 66 miscF. S.Francesco Saverio Pezzicoli, G.Guillaume Charpiat and F. P.François P. Landes. SE(3)-equivariant Graph Neural Networks for Learning Glassy Liquids Representations.2022
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• 67 miscZ.Zhen Xu, L.Lanning Wei, H.Huan Zhao, R.Rex Ying, Q.Quanming Yao, W.-W.Wei-Wei Tu and I.Isabelle Guyon. BRIDGING THE GAP OF AUTOGRAPH BETWEEN ACADEMIA AND INDUSTRY: ANALYSING AUTOGRAPH CHALLENGE AT KDD CUP 2020.April 2022
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Other scientific publications

• 68 inproceedingsI.Isabelle Hoxha, C.Clark Bäker, S.Sylvain Chevallier, S.Stefan Glasauer and M.-A.Michel-Ange Amorim. Generating stimulus anticipation from stimulus and prediction history.BC 2022 - Bernstein ConferenceBerlin, GermanySeptember 2022
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