Keywords
Computer Science and Digital Science
 A3.4. Machine learning and statistics
 A5.4. Computer vision
 A6.2. Scientific computing, Numerical Analysis & Optimization
 A7.1. Algorithms
 A8.2. Optimization
 A9.2. Machine learning
Other Research Topics and Application Domains
 B9.5.6. Data science
1 Team members, visitors, external collaborators
Research Scientists
 Francis Bach [Team leader, Inria, Senior Researcher, HDR]
 Laurent El Ghaoui [Inria, Advanced Research Position, from May 2021 until Jun 2021, HDR]
 Alessandro Rudi [Inria, Researcher]
 Umut Simsekli [Inria, Researcher]
 Adrien Taylor [Inria, Starting Research Position]
 Alexandre d'Aspremont [CNRS, Senior Researcher]
PostDoctoral Fellows
 Martin Arjovsky [Inria]
 Pierre Cyril Aubin [Inria, from Sep 2021]
 Seyed Daneshmand [Inria]
 Hans Kersting [Inria]
 Ziad Kobeissi [Institut Louis Bachelier]
 Boris Muzellec [Inria]
 Anant Raj [Inria, from Nov 2021]
 Blake Woodworth [Inria, from Oct 2021]
PhD Students
 Mathieu Barre [École Normale Supérieure de Paris, until Oct 2021]
 Eloise Berthier [DGA]
 Raphael Berthier [Inria, until Sep 2021]
 Gaspard Beugnot [Inria, from Apr 2021]
 Vivien Cabannes [Inria]
 Radu Alexandru Dragomir [École polytechnique, until Oct 2021]
 Bertille Follain [École Normale Supérieure de Paris, from Sep 2021]
 Gautier Izacard [CNRS]
 Remi Jezequel [École Normale Supérieure de Paris]
 Marc Lambert [DGA]
 Clement Lezane [Université de Twente  PaysBas, from Sep 2021]
 Ulysse MarteauFerey [Inria]
 Gregoire Mialon [Inria]
 Celine Moucer [Ecole normale supérieure ParisSaclay, from Sep 2021]
 Alex Nowak Vila [Inria, until Sep 2021]
 Benjamin PaulDuboisTaine [Université ParisSaclay, from Sep 2021]
 Manon Romain [CNRS]
 Lawrence Stewart [Inria, from Oct 2021]
Technical Staff
 Loïc Estève [Inria, Engineer]
 Anant Raj [Inria, Engineer, from Sep 2021 until Oct 2021]
Interns and Apprentices
 Theophile Cantelobre [Inria, from Apr 2021 until Sep 2021]
 Megi Dervishi [Inria, from Mar 2021 until May 2021]
 Clement Lezane [Inria, from May 2021 until Sep 2021]
 Tomas Rigaux [Inria, until Feb 2021]
 Louis Romain Roux [Inria, from Apr 2021 until Oct 2021]
 Lawrence Stewart [Inria, from Apr 2021 until Sep 2021]
 Badr Youbi Idrissi [Inria, from Sep 2021 until Oct 2021]
Administrative Assistants
 Helene Bessin Rousseau [Inria, from Feb 2021]
 Helene Milome [Inria]
 Scheherazade Rouag [Inria, until Apr 2021]
Visiting Scientist
 Benjamin PaulDuboisTaine [Université ParisSaclay, from Apr 2021 until Aug 2021]
External Collaborator
 Laurent El Ghaoui [University of CaliforniaBerkeley, from Jul 2021, HDR]
2 Overall objectives
2.1 Statement
Machine learning is a recent scientific domain, positioned between applied mathematics, statistics and computer science. Its goals are the optimization, control, and modelisation of complex systems from examples. It applies to data from numerous engineering and scientific fields (e.g., vision, bioinformatics, neuroscience, audio processing, text processing, economy, finance, etc.), the ultimate goal being to derive general theories and algorithms allowing advances in each of these domains. Machine learning is characterized by the high quality and quantity of the exchanges between theory, algorithms and applications: interesting theoretical problems almost always emerge from applications, while theoretical analysis allows the understanding of why and when popular or successful algorithms do or do not work, and leads to proposing significant improvements.
Our academic positioning is exactly at the intersection between these three aspects—algorithms, theory and applications—and our main research goal is to make the link between theory and algorithms, and between algorithms and highimpact applications in various engineering and scientific fields, in particular computer vision, bioinformatics, audio processing, text processing and neuroimaging.
Machine learning is now a vast field of research and the team focuses on the following aspects: supervised learning (kernel methods, calibration), unsupervised learning (matrix factorization, statistical tests), parsimony (structured sparsity, theory and algorithms), and optimization (convex optimization, bandit learning). These four research axes are strongly interdependent, and the interplay between them is key to successful practical applications.
3 Research program
3.1 Supervised Learning
This part of our research focuses on methods where, given a set of examples of input/output pairs, the goal is to predict the output for a new input, with research on kernel methods, calibration methods, and multitask learning.
3.2 Unsupervised Learning
We focus here on methods where no output is given and the goal is to find structure of certain known types (e.g., discrete or lowdimensional) in the data, with a focus on matrix factorization, statistical tests, dimension reduction, and semisupervised learning.
3.3 Parsimony
The concept of parsimony is central to many areas of science. In the context of statistical machine learning, this takes the form of variable or feature selection. The team focuses primarily on structured sparsity, with theoretical and algorithmic contributions.
3.4 Optimization
Optimization in all its forms is central to machine learning, as many of its theoretical frameworks are based at least in part on empirical risk minimization. The team focuses primarily on convex and bandit optimization, with a particular focus on largescale optimization.
4 Application domains
4.1 Applications for Machine Learning
Machine learning research can be conducted from two main perspectives: the first one, which has been dominant in the last 30 years, is to design learning algorithms and theories which are as generic as possible, the goal being to make as few assumptions as possible regarding the problems to be solved and to let data speak for themselves. This has led to many interesting methodological developments and successful applications. However, we believe that this strategy has reached its limit for many application domains, such as computer vision, bioinformatics, neuroimaging, text and audio processing, which leads to the second perspective our team is built on: Research in machine learning theory and algorithms should be driven by interdisciplinary collaborations, so that specific prior knowledge may be properly introduced into the learning process, in particular with the following fields:
 Computer vision: object recognition, object detection, image segmentation, image/video processing, computational photography. In collaboration with the Willow projectteam.
 Bioinformatics: cancer diagnosis, protein function prediction, virtual screening. In collaboration with Institut Curie.
 Text processing: document collection modeling, language models.
 Audio processing: source separation, speech/music processing.
 Neuroimaging: braincomputer interface (fMRI, EEG, MEG).
5 Highlights of the year
5.1 Awards
 Alessandro Rudi is recipient of the ERC Starting Grant. The ERC project is named REAL (947908) and corresponds to a grant of 1.5 millions € for the period 20212026.
 Outstanding paper award at NeurIPS 2021 for the paper Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms (Mathieu Even, Raphaël Berthier, Francis Bach, Nicolas Flammarion, Hadrien Hendrikx, Pierre Gaillard, Laurent Massoulié, Adrien Taylor).
 Testoftime award at NeurIPS 2021 for the paper Online Learning for Latent Dirichlet Allocation by Matthew Hoffman, David Blei, and Francis Bach.
5.2 Books
 Alexandre d'Aspremont, Damien Scieur and Adrien Taylor (2021), “Acceleration Methods”, Foundations and Trends® in Optimization: Vol. 5: No. 12, pp 1245.
6 New software and platforms
6.1 New software
6.1.1 ProxASAGA

Keyword:
Optimization

Functional Description:
A C++/Python code implementing the methods in the paper "Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization", F. Pedregosa, R. Leblond and S. LacosteJulien, Advances in Neural Information Processing Systems (NIPS) 2017. Due to their simplicity and excellent performance, parallel asynchronous variants of stochastic gradient descent have become popular methods to solve a wide range of largescale optimization problems on multicore architectures. Yet, despite their practical success, support for nonsmooth objectives is still lacking, making them unsuitable for many problems of interest in machine learning, such as the Lasso, group Lasso or empirical risk minimization with convex constraints. In this work, we propose and analyze ProxASAGA, a fully asynchronous sparse method inspired by SAGA, a variance reduced incremental gradient algorithm. The proposed method is easy to implement and significantly outperforms the state of the art on several nonsmooth, largescale problems. We prove that our method achieves a theoretical linear speedup with respect to the sequential version under assumptions on the sparsity of gradients and blockseparability of the proximal term. Empirical benchmarks on a multicore architecture illustrate practical speedups of up to 12x on a 20core machine.
 URL:

Contact:
Fabian Pedregosa
6.1.2 objectstatesaction

Keyword:
Computer vision

Functional Description:
Code for the paper Joint Discovery of Object States and Manipulation Actions, ICCV 2017: Many human activities involve object manipulations aiming to modify the object state. Examples of common state changes include full/empty bottle, open/closed door, and attached/detached car wheel. In this work, we seek to automatically discover the states of objects and the associated manipulation actions. Given a set of videos for a particular task, we propose a joint model that learns to identify object states and to localize statemodifying actions. Our model is formulated as a discriminative clustering cost with constraints. We assume a consistent temporal order for the changes in object states and manipulation actions, and introduce new optimization techniques to learn model parameters without additional supervision. We demonstrate successful discovery of seven manipulation actions and corresponding object states on a new dataset of videos depicting reallife object manipulations. We show that our joint formulation results in an improvement of object state discovery by action recognition and vice versa.
 URL:
 Publication:

Contact:
JeanBaptiste Alayrac

Participants:
JeanBaptiste Alayrac, Josef Sivic, Ivan Laptev, Simon LacosteJulien
7 New results
7.1 On the Effectiveness of Richardson Extrapolation in Data Science
Richardson extrapolation is a classical technique from numerical analysis that can improve the approximation error of an estimation method by combining linearly several estimates obtained from different values of one of its hyperparameters without the need to know in details the inner structure of the original estimation method. The main goal of this paper is to study when Richardson extrapolation can be used within data science beyond the existing applications to stepsize adaptations in stochastic gradient descent. We identify two situations where Richardson interpolation can be useful: (1) when the hyperparameter is the number of iterations of an existing iterative optimization algorithm with applications to averaged gradient descent and Frank–Wolfe algorithms (where we obtain asymptotically rates of $O(1/{k}^{2})$ on polytopes, where k is the number of iterations) and (2) when it is a regularization parameter with applications to Nesterov smoothing techniques for minimizing nonsmooth functions (where we obtain asymptotically rates close to $O(1/{k}^{2})$ for nonsmooth functions) and kernel ridge regression. In all these cases, we show that extrapolation techniques come with no significant loss in performance but with sometimes strong gains, and we provide theoretical justifications based on asymptotic developments for such gains, as well as empirical illustrations on classical problems from machine learning.
7.2 Batch Normalization Orthogonalizes Representations in Deep Random Networks
This paper underlines a subtle property of batchnormalization (BN): Successive batch normalizations with random linear transformations make hidden representations increasingly orthogonal across layers of a deep neural network. We establish a nonasymptotic characterization of the interplay between depth, width, and the orthogonality of deep representations. More precisely, under a mild assumption, we prove that the deviation of the representations from orthogonality rapidly decays with depth up to a term inversely proportional to the network width. This result has two main implications: 1) Theoretically, as the depth grows, the distribution of the representation –after the linear layers– contracts to a Wasserstein2 ball around an isotropic Gaussian distribution. Furthermore, the radius of this Wasserstein ball shrinks with the width of the network. 2) In practice, the orthogonality of the representations directly influences the performance of stochastic gradient descent (SGD). When representations are initially aligned, we observe SGD wastes many iterations to orthogonalize representations before the classification. Nevertheless, we experimentally show that starting optimization from orthogonal representations is sufficient to accelerate SGD, with no need for BN.
7.3 A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip
We introduce the “continuized” Nesterov acceleration, a close variant of Nesterov acceleration whose variables are indexed by a continuous time parameter. The two variables continuously mix following a linear ordinary differential equation and take gradient steps at random times. This continuized variant benefits from the best of the continuous and the discrete frameworks: as a continuous process, one can use differential calculus to analyze convergence and obtain analytical expressions for the parameters; and a discretization of the continuized process can be computed exactly with convergence rates similar to those of Nesterov original acceleration. We show that the discretization has the same structure as Nesterov acceleration, but with random parameters. We provide continuized Nesterov acceleration under deterministic as well as stochastic gradients, with either additive or multiplicative noise. Finally, using our continuized framework and expressing the gossip averaging problem as the stochastic minimization of a certain energy function, we provide the first rigorous acceleration of asynchronous gossip algorithms
7.4 A Dimensionfree Computational Upperbound for Smooth Optimal Transport Estimation
It is wellknown that plugin statistical estimation of optimal transport suffers from the curse of dimensionality. Despite recent efforts to improve the rate of estimation with the smoothness of the problem, the computational complexity of these recently proposed methods still degrade exponentially with the dimension. In this paper, thanks to an infinitedimensional sumofsquares representation, we derive a statistical estimator of smooth optimal transport which achieves a precision $\epsilon $ from $\tilde{O}\left({\epsilon}^{2}\right)$, independent and identically distributed samples from the distributions, for a computational cost of $\tilde{O}\left({\epsilon}^{4}\right)$ when the smoothness increases, hence yielding dimensionfree statistical and computational rates, with potentially exponentially dimensiondependent constants.
7.5 Fast rates in structured prediction
Discrete supervised learning problems such as classification are often tackled by introducing a continuous surrogate problem akin to regression. Bounding the original error, between estimate and solution, by the surrogate error endows discrete problems with convergence rates already shown for continuous instances. Yet, current approaches do not leverage the fact that discrete problems are essentially predicting a discrete output when continuous problems are predicting a continuous value. In this paper, we tackle this issue for general structured prediction problems, opening the way to “super fast” rates, that is, convergence rates for the excess risk faster than ${n}^{1}$, where n is the number of observations, with even exponential rates with the strongest assumptions. We first illustrate it for predictors based on nearest neighbors, generalizing rates known for binary classification to any discrete problem within the framework of structured prediction. We then consider kernel ridge regression where we improve known rates in ${n}^{1/4}$ to arbitrarily fast rates, depending on a parameter characterizing the hardness of the problem, thus allowing, under smoothness assumptions, to bypass the curse of dimensionality.
7.6 Disambiguation of weak supervision with exponential convergence rates
Machine learning approached through supervised learning requires expensive annotation of data. This motivates weakly supervised learning, where data are annotated with incomplete yet discriminative information. In this project, we focus on partial labelling, an instance of weak supervision where, from a given input, we are given a set of potential targets. We review a disambiguation principle to recover full supervision from weak supervision, and propose an empirical disambiguation algorithm. We prove exponential convergence rates of our algorithm under classical learnability assumptions, and we illustrate the usefulness of our method on practical examples
7.7 Deep Equals Shallow for ReLU Networks in Kernel Regimes
Deep networks are often considered to be more expressive than shallow ones in terms of approximation. Indeed, certain functions can be approximated by deep networks provably more efficiently than by shallow ones, however, no tractable algorithms are known for learning such deep models. Separately, a recent line of work has shown that deep networks trained with gradient descent may behave like (tractable) kernel methods in a certain overparameterized regime, where the kernel is determined by the architecture and initialization, and this paper focuses on approximation for such kernels. We show that for ReLU activations, the kernels derived from deep fullyconnected networks have essentially the same approximation properties as their “shallow” twolayer counterpart, namely the same eigenvalue decay for the corresponding integral operator. This highlights the limitations of the kernel framework for understanding the benefits of such deep architectures. Our main theoretical result relies on characterizing such eigenvalue decays through differentiability properties of the kernel function, which also easily applies to the study of other kernels defined on the sphere.
7.8 Explicit Regularization of Stochastic Gradient Methods through Duality
We consider stochastic gradient methods under the interpolation regime where a perfect fit can be obtained (minimum loss at each observation). While previous work highlighted the implicit regularization of such algorithms, we consider an explicit regularization framework as a minimum Bregman divergence convex feasibility problem. Using convex duality, we propose randomized Dykstrastyle algorithms based on randomized dual coordinate ascent. For nonaccelerated coordinate descent, we obtain an algorithm which bears strong similarities with (nonaveraged) stochastic mirror descent on specific functions, as it is equivalent for quadratic objectives, and equivalent in the early iterations for more general objectives. It comes with the benefit of an explicit convergence theorem to a minimum norm solution. For accelerated coordinate descent, we obtain a new algorithm that has better convergence properties than existing stochastic gradient methods in the interpolating regime. This leads to accelerated versions of the perceptron for generic ${\ell}_{p}$norm regularizers, which we illustrate in experiments.
7.9 The recursive variational Gaussian approximation (RVGA)
We consider the problem of computing a Gaussian approximation to the posterior distribution of a parameter given $N$ observations and a Gaussian prior. Owing to the need of processing large sample sizes $N$, a variety of approximate tractable methods revolving around online learning have flourished over the past decades. In the present work, we propose to use variational inference (VI) to compute a Gaussian approximation to the posterior through a single pass over the data. Our algorithm is a recursive version of the variational Gaussian approximation we have called recursive variational Gaussian approximation (RVGA). We start from the prior, and for each observation we compute the nearest Gaussian approximation in the sense of KullbackLeibler divergence to the posterior given this observation. In turn, this approximation is considered as the new prior when incorporating the next observation. This recursive version based on a sequence of optimal Gaussian approximations leads to a novel implicit update scheme which resembles the online Newton algorithm, and which is shown to boil down to the Kalman filter for Bayesian linear regression. In the context of Bayesian logistic regression the implicit scheme may be solved, and the algorithm is shown to perform better than the extended Kalman filter, while being far less computationally demanding than its sampling counterparts.
7.10 Restarting FrankWolfe
Conditional Gradients (aka FrankWolfe algorithms) form a classical set of methods for constrained smooth convex minimization due to their simplicity, the absence of projection step, and competitive numerical performance. While the vanilla FrankWolfe algorithm only ensures a worstcase rate of O(1/epsilon), various recent results have shown that for strongly convex functions, the method can be slightly modified to achieve linear convergence. However, this still leaves a huge gap between sublinear O(1/epsilon) convergence and linear O(log 1/epsilon) convergence to reach an epsapproximate solution. Here, we present a new variant of Conditional Gradients, that can dynamically adapt to the function's geometric properties using restarts and thus smoothly interpolates between the sublinear and linear regimes.
7.11 Approximation Bounds for Sparse Programs
We show that sparsityconstrained optimization problems over low dimensional spaces tend to have a small duality gap. We use the ShapleyFolkman theorem to derive both datadriven bounds on the duality gap, and an efficient primalization procedure to recover feasible points satisfying these bounds. These error bounds are proportional to the rate of growth of the objective with the target cardinality k, which means in particular that the relaxation is nearly tight as soon as k is large enough so that only uninformative features are added.
7.12 Linear Bandits on Uniformly Convex Sets
Linear bandit algorithms yield two types of structural assumptions lead to better pseudoregret bounds. When $K$ is the simplex or an ${\ell}_{p}$ ball with p in $]1,2]$, there exist bandits algorithms with $O\left(\sqrt{nT}\right)$ pseudoregret bounds. Here, we derive bandit algorithms for some strongly convex sets beyond ${l}_{p}$ balls that enjoy pseudoregret bounds of $O\left(\sqrt{nT}\right)$, which answers an open question from (BCB12, S 5.5). Interestingly, when the action set is uniformly convex but not necessarily strongly convex, we obtain pseudoregret bounds with a dimension dependency smaller than $O\left(\sqrt{n}\right)$. However, this comes at the expense of asymptotic rates in $T$ varying between $O\left(\sqrt{T}\right)$ and $O\left(T\right)$.
7.13 Local and Global Uniform Convexity Conditions
We review various characterizations of uniform convexity and smoothness on norm balls in finitedimensional spaces and connect results stemming from the geometry of Banach spaces with scaling inequalities used in analyzing the convergence of optimization methods. In particular, we establish local versions of these conditions to provide sharper insights on a recent body of complexity results in learning theory, online learning, or offline optimization, which rely on the strong convexity of the feasible set. While they have a significant impact on complexity, these strong convexity or uniform convexity properties of feasible sets are not exploited as thoroughly as their functional counterparts, and this work is an effort to correct this imbalance. We conclude with some practical examples in optimization and machine learning where leveraging these conditions and localized assumptions lead to new complexity results.
7.14 Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms
Understanding generalization in deep learning has been one of the major challenges in statistical learning theory over the last decade. While recent work has illustrated that the dataset and the training algorithm must be taken into account in order to obtain meaningful generalization bounds, it is still theoretically not clear which properties of the data and the algorithm determine the generalization performance. In this study, we approach this problem from a dynamical systems theory perspective and represent stochastic optimization algorithms as random iterated function systems (IFS). Well studied in the dynamical systems literature, under mild assumptions, such IFSs can be shown to be ergodic with an invariant measure that is often supported on sets with a fractal structure. As our main contribution, we prove that the generalization error of a stochastic optimization algorithm can be bounded based on the `complexity' of the fractal structure that underlies its invariant measure. Then, by leveraging results from dynamical systems theory, we show that the generalization error can be explicitly linked to the choice of the algorithm (e.g., stochastic gradient descent – SGD), algorithm hyperparameters (e.g., stepsize, batchsize), and the geometry of the problem (e.g., Hessian of the loss). We further specialize our results to specific problems (e.g., linear/logistic regression, one hiddenlayered neural networks) and algorithms (e.g., SGD and preconditioned variants), and obtain analytical estimates for our bound. For modern neural networks, we develop an efficient algorithm to compute the developed bound and support our theory with various experiments on neural networks.
7.15 PSD Representations for Effective Probability Models
Finding a good way to model probability densities is key to probabilistic inference. An ideal model should be able to concisely approximate any probability while being also compatible with two main operations: multiplications of two models (product rule) and marginalization with respect to a subset of the random variables (sum rule). In this work, we show that a recently proposed class of positive semidefinite (PSD) models for nonnegative functions is particularly suited to this end. In particular, we characterize both approximation and generalization capabilities of PSD models, showing that they enjoy strong theoretical guarantees. Moreover, we show that we can perform efficiently both sum and product rule in closed form via matrix operations, enjoying the same versatility of mixture models. Our results open the way to applications of PSD models to density estimation, decision theory and inference.
7.16 Sampling from Arbitrary Functions via PSD Models
In many areas of applied statistics and machine learning, generating an arbitrary number of independent and identically distributed (i.i.d.) samples from a given distribution is a key task. When the distribution is known only through evaluations of the density, current methods either scale badly with the dimension or require very involved implementations. Instead, we take a twostep approach by first modeling the probability distribution and then sampling from that model. We use the recently introduced class of positive semidefinite (PSD) models, which have been shown to be efficient for approximating probability densities. We show that these models can approximate a large class of densities concisely using few evaluations, and present a simple algorithm to effectively sample from these models. We also present preliminary empirical results to illustrate our assertions.
7.17 Mixability made efficient: Fast online multiclass logistic regression
Mixability has been shown to be a powerful tool to obtain algorithms with optimal regret. However, the resulting methods often suffer from high computational complexity which has reduced their practical applicability. For example, in the case of multiclass logistic regression, the aggregating forecaster (Foster et al. (2018)) achieves a regret of $O(log(Bn\left)\right)$ whereas Online Newton Step achieves $O({e}^{B}log\left(n\right))$ obtaining a double exponential gain in B (a bound on the norm of comparative functions). However, this high statistical performance is at the price of a prohibitive computational complexity $O\left({n}^{37}\right)$.
7.18 Beyond Tikhonov: Faster Learning with SelfConcordant Losses via Iterative Regularization
The theory of spectral filtering is a remarkable tool to understand the statistical properties of learning with kernels. For least squares, it allows to derive various regularization schemes that yield faster convergence rates of the excess risk than with Tikhonov regularization. This is typically achieved by leveraging classical assumptions called source and capacity conditions, which characterize the difficulty of the learning task. In order to understand estimators derived from other loss functions, MarteauFerey et al. have extended the theory of Tikhonov regularization to generalized self concordant loss functions (GSC), which contain, e.g., the logistic loss. In this paper, we go a step further and show that fast and optimal rates can be achieved for GSC by using the iterated Tikhonov regularization scheme, which is intrinsically related to the proximal point method in optimization, and overcomes the limitation of the classical Tikhonov regularization.
8 Bilateral contracts and grants with industry
8.1 Bilateral grants with industry
 Alexandre d’Aspremont, Francis Bach, Martin Jaggi (EPFL): Google Focused award.
 Francis Bach: Gift from Facebook AI Research.
 Alexandre d’Aspremont: fondation AXA, "Mécénat scientifique", optimisation & machine learning.
9 Partnerships and cooperations
9.1 International initiatives
9.1.1 Associate Teams in the framework of an Inria International Lab or in the framework of an Inria International Program
4TUNE

Title:
Adaptive, Efficient, Provable and Flexible Tuning for Machine Learning

Duration:
2020 >

Coordinator:
Peter Grünwald (pdg@cwi.nl)

Partners:
 CWI

Inria contact:
Francis Bach
 Summary:
FOAM

Title:
FirstOrder Accelerated Methods for Machine Learning.

Duration:
2020 >

Coordinator:
Cristobal Guzman (crguzmanp@mat.uc.cl)

Partners:
 Pontificia Universidad Católica de Chile

Inria contact:
Alexandre D'Aspremont
 Summary:
9.2 European initiatives
9.2.1 Horizon Europe
 Alessandro Rudi is recipient of the ERC Starting Grant. The ERC project is named REAL (947908) and corresponds to a grant of 1.5 millions € for the period 20212026.
9.3 National initiatives
 Alexandre d'Aspremont: IRIS, PSL “Science des données, données de la science”.
10 Dissemination
10.0.1 Journal
Member of the editorial boards
 Alexandre d'Aspremont, associate editor for SIAM Journal on the Mathematics of Data Science.
 Alexandre d'Aspremont, associate editor for SIAM Journal on Optimization.
 Alexandre d'Aspremont, associate editor for Mathematics of Operations Research.
 Francis Bach, coeditorinchief, Journal of Machine Learning Research
 Francis Bach, associate Editor, Mathematical Programming
 Francis Bach, associate editor, Foundations of Computational Mathematics (FoCM)
Reviewer  reviewing activities
 Alessandro Rudi: area chair for “International Conference on Machine Learning 2021”
 Alessandro Rudi: area chair for “Advances in Neural Information Processing Systems 2021”
 Adrien Taylor: reviewer for “Conference on Learning Theory 2021”.
 Adrien Taylor: reviewer for “Computational Optimization And Applications”.
 Adrien Taylor: reviewer for “IMA Journal on Numerical Analysis”.
 Adrien Taylor: reviewer for “Journal of Optimization Theory and Applications”.
 Adrien Taylor: reviewer for “Mathematical Programming”.
 Adrien Taylor: reviewer for “SIAM Journal on Optimization”.
 Umut Simsekli: area chair for “International Conference on Machine Learning 2021”
 Umut Simsekli: area chair for “Advances in Neural Information Processing Systems 2021”
10.0.2 Invited talks
 Adrien Taylor: invited talk at Europt (July 2021, online).
 Adrien Taylor: invited talk at the “AllRussian optimization seminar” (May 2021, online).
 Adrien Taylor: invited talk at the “Optimization without Borders” workshop (July 2021, Sochi/online).
 Adrien Taylor: invited talk at EPFL (October 2021, Lausanne).
 Adrien Taylor: invited talk at SUTD (December 2021, Singapore/Online).
 Alexandre d'Aspremont: invited talk at Cornell ORIE.
 Alexandre d'Aspremont: invited talk at MIT OR.
 Alexandre d'Aspremont: invited talk at "Optimization without Borders" workshop, Sochi.
 Francis Bach: invited talk at Caltech University (January 2021, online)
 Francis Bach: invited talk at Journées Math / IA (March 2021, online)
 Francis Bach: invited talk at Stanford University (April 2021, online)
 Francis Bach: invited talk at Georgia Tech University (September 2021, online)
 Francis Bach: invited talk at IMSI, Chicago (October 2021, online)
 Francis Bach: invited talk at MIT (November 2021, online)
 Francis Bach: invited talk at University of Michigan (November 2021, online)
 Umut Simsekli: invited talk at University of California, Los Angeles  Max Planck Institute (February 2021, online)
 Umut Simsekli: invited talk at the Mathematical Statistics and Learning Workshop (June 2021, Barcelona)
 Umut Simsekli: invited talk at ESSEC Business School (November 2021, Cergy)
 Umut Simsekli: invited talk at Current Developments in MCMC Methods Workshop (December 2021, Warsaw)
 Umut Simsekli: invited talk at University of Bristol (November 2021, online)
 Alessandro Rudi: invited talk "Finding global minima via kernel approximations", RWTH Chair for Mathematics of Information Processing, RWTH Aachen University, 14 June 2021.
 Alessandro Rudi: invited talk "Towards energyaware ML From first principles", ECOINFO CNRS meeting, 21 June 2021.
 Alessandro Rudi: invited talk "Effective models for nonnegative functions", Mathematical Statistics and Learning 2021, Barcelona, 29 June 2021.
 Alessandro Rudi: invited talk "PSD models for Nonconvex optimization and beyond", Statistics Seminars, Sorbonne Université, 9 Nov 2021.
10.0.3 Leadership within the scientific community
 Francis Bach: president of the board of ICML
10.0.4 Research administration
 Francis Bach: Deputy Scientific director, Inria Paris
10.1 Teaching  Supervision  Juries
10.1.1 Teaching
 Master: Alexandre d'Aspremont, Optimisation Combinatoire et Convexe, avec Zhentao Li, (2015Present) cours magistraux 30h, Master M1, ENS Paris.
 Master: Alexandre d'Aspremont, Optimisation convexe: modélisation, algorithmes et applications cours magistraux 21h (2011Present), Master M2 MVA, ENS PS.
 Master : Francis Bach, Optimisation et apprentissage statistique, 20h, Master M2 (Mathématiques de l'aléatoire), Université ParisSud, France.
 Master : Francis Bach, Learning theory from first principles, 27h, Master M2 MASH, Université Paris Dauphine PSL, France.
 Master : Francis Bach, Machine Learning, 20h, Master ICFP (Physique), Université PSL.
 Master: Alessandro Rudi, Umut Simsekli. Introduction to Machine Learning, 52h, L3, ENS, Paris.
10.1.2 Supervision
 PhD in progress: Grégoire Mialon, Sample Selection Methods, 2018, Alexandre d'Aspremont (joint with Julien Mairal)
 PhD in progress: Manon Romain, Causal Inference Algorithms, 2020, Alexandre d'Aspremont
 PhD in progress: Theophile Cantelobre, supervised by Alessandro Rudi, Benjamin Guedj, Carlo Ciliberto (UCL).
 PhD in progress: Gaspard Beugnot, supervised by Alessandro Rudi, Julien Mairal.
 PhD in progress: Ulysse Marteau Ferey, supervised by Alessandro Rudi and Francis Bach.
 PhD in progress: Vivien Cabannes, supervised by Francis Bach and Alessandro Rudi.
 PhD in progress: Eloise Berthier, supervised by Francis Bach.
 PhD in progress: Theo Ryffel, supervised by Francis Bach and David Pointcheval.
 PhD in progress: Rémi Jezequel, supervised by Pierre Gaillard and Alessandro Rudi.
 PhD in progress: Antoine Bambade, supervised by JeanPonce (Willow), Justin Carpentier (Willow), and Adrien Taylor.
 PhD in progress: Marc Lambert, supervised by Francis Bach and Silvère Bonnabel.
 PhD in progress: Ivan Lerner, coadvised with Anita Burgun et Antoine Neuraz.
 PhD in progress: Lawrence Stewart, coadvised by Francis Bach and JeanPhilippe Vert.
 PhD in progress: Céline Moucer, supervised by Adrien Taylor and Francis Bach
 PhD in progress: Bertille Follain, supervised by Umut Simsekli and Francis Bach
 PhD defended: Raphaël Berthier, supervised by Francis Bach and Pierre Gaillard.
 PhD defended: Radu  Dragomir Alexandru, Bregman Gradient Methods, Alexandre d'Aspremont (joint with Jérôme Bolte)
 PhD defended: Mathieu Barré, Accelerated Polyak Methods, Alexandre d'Aspremont
 PhD defended: Alex NowakVila, supervised by Francis Bach and Alessandro Rudi.
 PhD defended: Hadrien Hendrikx, supervised by Francis Bach and Laurent Massoulié.
10.1.3 Juries
 Francis Bach: HDR committee of Emilie Kaufmann
 Francis Bach: HDR committee of Aurélien Bellet
 Francis Bach: HDR committee of Samuel Vaiter
 Umut Simsekli: PhD committee of FrançoisPierre Paty
10.2 Popularization
10.2.1 Interventions
 Francis Bach: Keynote talk at GPAI summit (November 2021)
 Francis Bach: Presentation on scientific challenges of AI, Ecole de Guerre (September 2021)
11 Scientific production
11.1 Major publications
 1 unpublishedNonparametric Models for Nonnegative Functions.July 2020, working paper or preprint
 2 articleSharpness, Restart and Acceleration.SIAM Journal on Optimization301October 2020, 262289
11.2 Publications of the year
International journals
 3 articleOn the Effectiveness of Richardson Extrapolation in Data Science.SIAM Journal on Mathematics of Data Science342021, 12511277
 4 articleRanking and synchronization from pairwise measurements via SVD.Journal of Machine Learning Research2219February 2021, 163
 5 articleOptimal Complexity and Certification of Bregman FirstOrder Methods.Mathematical ProgrammingApril 2021
 6 articleThe recursive variational Gaussian approximation (RVGA).Statistics and Computing2021
International peerreviewed conferences
 7 inproceedingsBeyond Tikhonov: Faster Learning with SelfConcordant Losses via Iterative Regularization.NeurIPS 2021 – 35th Annual Conference on Neural Information Processing SystemsAdvances in Neural Information Processing Systems 34Virtual, FranceDecember 2021, 137
 8 inproceedingsDeep Equals Shallow for ReLU Networks in Kernel Regimes.ICLR 2021  International Conference on Learning RepresentationsVirtual, AustriaMay 2021, 122
 9 inproceedingsIntrinsic Dimension, Persistent Homology and Generalization in Neural Networks.NeurIPS 2021  Thirtyfifth Conference on Neural Information Processing SystemsVirtual, FranceDecember 2021
 10 inproceedingsFast Rates for Structured Prediction.COLT 2021  34th Annual Conference on Learning Theory134Proceedings of Machine Learning ResearchBoulder, Colorado, United StatesJuly 2021
 11 inproceedingsOvercoming the curse of dimensionality with Laplacian regularization in semisupervised learning.NeurIPS 2021  Thirtyfifth conference on Neural Information Processing Systems (NeurIPS)Online, Unknown RegionDecember 2021
 12 inproceedingsDisambiguation of Weak Supervision leading to Exponential Convergence rates.Proceedings of the 38th International Conference on Machine LearningICML 2021  38th International Conference on Machine Learning139Virtual, FranceJuly 2021, 11471157
 13 inproceedingsFractal Structure and Generalization Properties of Stochastic Optimization Algorithms.NeurIPS 2021  Thirtyfifth Conference on Neural Information Processing SystemsVirtual, FranceDecember 2021
 14 inproceedingsAsymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections.ICML 2021  Thirtyeighth annual conference International Conference on Machine LearningVirtual, FranceJuly 2021
 15 inproceedingsSelfSupervised VQVAE for OneShot Music Style Transfer.ICASSP 2021  IEEE International Conference on Acoustics, Speech and Signal ProcessingICASSP 2021  IEEE International Conference on Acoustics, Speech and Signal ProcessingToronto / Virtual, CanadaJune 2021
 16 inproceedingsFast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction.ICML 2021 38th International Conference on Machine Learning139Proceedings of the 38th International Conference on Machine Learningvirtual, United StatesJuly 2021, 28152825
 17 inproceedingsA Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip.Advances in Neural Information Processing Systems 34NeurIPS 2021  35th Conference on Neural Information Processing SystemsSydney (virtual), AustraliaMorgan Kaufmann PublishersDecember 2021, 132
 18 inproceedingsThe HeavyTail Phenomenon in SGD.ICML2021  Thirtyeighth annual conference on International Conference on Machine LearningVirtual, FranceJuly 2021
 19 inproceedingsMixability made efficient: Fast online multiclass logistic regression.NeurIPS 2021. Thirtyfifth Conference on Neural Information Processing SystemsOnline, FranceDecember 2021
 20 inproceedingsRelative Positional Encoding for Transformers with Linear Complexity.ICML 2021  38th International Conference on Machine LearningProceedings of the 38th International Conference on Machine LearningVirtual Only, United StatesJuly 2021
 21 inproceedingsA Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention.ICLR 2021  The Ninth International Conference on Learning RepresentationsVirtual, FranceMay 2021
 22 inproceedingsFast Approximation of the SlicedWasserstein Distance Using Concentration of Random Projections.NeurIPS 2021  Thirtyfifth Conference on Neural Information Processing SystemsVirtual, FranceDecember 2021
 23 inproceedingsConvergence Rates of Stochastic Gradient Descent under Infinite Noise Variance.NeurIPS 2021  Thirtyfifth Conference on Neural Information Processing SystemsVirtual, FranceDecember 2021
 24 inproceedingsRevisiting the Role of Euler Numerical Integration on Acceleration and Stability in Convex Optimization.AISTATS 2021  24th International Conference on Artificial Intelligence and StatisticsVirtual, Unknown RegionApril 2021
 25 inproceedingsRethinking the Variational Interpretation of Accelerated Optimization Methods.NeurrIPS 2021Virtual, Unknown RegionNovember 2021
 26 inproceedingsBatch Normalization Orthogonalizes Representations in Deep Random Networks.NeurIPS 2021  35th Conference on Neural Information Processing SystemsVirtual, FranceDecember 2021
Conferences without proceedings
 27 inproceedingsFast and Robust Stability Region Estimation for Nonlinear Dynamical Systems.European Control Conference (ECC) 2021Rotterdam, NetherlandsJune 2021
 28 inproceedingsDistilling Knowledge from Reader to Retriever for Question Answering.ICLR 2021  9th International Conference on Learning RepresentationsVienna, AustriaMay 2021
 29 inproceedingsLeveraging Passage Retrieval with Generative Models for Open Domain Question Answering.EACL 2021  16th Conference of the European Chapter of the Association for Computational LinguisticsKiev, UkraineAssociation for Computational LinguisticsApril 2021, 874880
 30 inproceedingsA Dimensionfree Computational Upperbound for Smooth Optimal Transport Estimation.COLT 2021  34th Annual Conference on Learning TheoryBoulder, United StatesAugust 2021
Doctoral dissertations and habilitation theses
 31 thesisBregman Gradient Methods for RelativelySmooth Optimization.UT1 CapitoleSeptember 2021
 32 thesisAccelerated Methods for Distributed Optimization.PSLSeptember 2021
Reports & preprints
 33 miscApproximation Bounds for Sparse Programs.March 2021
 34 miscGradient Descent on Infinitely Wide Neural Networks: Global Convergence and Generalization.October 2021
 35 miscAveraging Atmospheric Gas Concentration Data using Wasserstein Barycenters.March 2021
 36 miscA note on approximate accelerated forwardbackward methods with absolute and relative errors, and possibly strongly convex objectives.October 2021
 37 miscA Continuized View on Nesterov Acceleration.February 2021
 38 miscInfiniteDimensional SumsofSquares for Optimal Control.October 2021
 39 miscLearning Output Embeddings in Structured Prediction.July 2021
 40 miscAcceleration Methods.March 2021
 41 miscMusic Source Separation in the Waveform Domain.April 2021
 42 miscOn the oracle complexity of smooth strongly convex minimization.March 2021
 43 miscGraphbased Approximate Message Passing Iterations.October 2021
 44 miscSuperAcceleration with Cyclical Stepsizes.October 2021
 45 miscLocal and Global Uniform Convexity Conditions.March 2021
 46 miscLinear Bandits on Uniformly Convex Sets.October 2021
 47 miscThe limitedmemory recursive variational Gaussian approximation (LRVGA).December 2021
 48 miscGlobal assessment of oil and gas methane ultraemitters.October 2021
 49 miscSampling from Arbitrary Functions via PSD Models.October 2021
 50 miscGraphiT: Encoding Graph Structure in Transformers.June 2021
 51 miscA Note on Optimizing Distributions using Kernel Mean Embeddings.June 2021
 52 miscLearning PSDvalued functions using kernel sumsofsquares.November 2021
 53 miscNearoptimal estimation of smooth transport maps with kernel sumsofsquares.December 2021
 54 miscA Bregman Method for Structure Learning on Sparse Directed Acyclic Graphs.March 2021
 55 miscAn optimal gradient method for smooth (possibly strongly) convex minimization.March 2021
 56 miscTowards Noiseadaptive, Problemadaptive Stochastic Gradient Descent.November 2021
 57 miscCounterfactual Learning of Stochastic Policies with Continuous Actions: from Models to Offline Evaluation.August 2021