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 high-impact applications in various engineering and scientific fields, in particular computer vision, bioinformatics, audio processing, text processing and neuro-imaging.

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.

The SIERRA project-team was created on January, 1 , 2011.

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 multi-task 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 low-dimensional) in the data, with a focus on matrix factorization, statistical tests, dimension reduction, and semi-supervised learning.

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 (this is the main topic of the ERC starting investigator grant awarded to F. Bach).

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 large-scale optimization.

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, neuro-imaging, 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: objet recognition, object detection, image segmentation, image/video processing, computational photography.

Bioinformatics: cancer diagnosis, protein function prediction, virtual screening.

Text processing: document collection modeling, language models.

Audio processing: source separation, speech/music processing.

Neuro-imaging: brain-computer interface (fMRI, EEG, MEG).

Generates Rd files from R source code with comments, providing for quick, sustainable package development. The syntax keeps code and documentation close together, and is inspired by the Don't Repeat Yourself principle.

See also the web page
http://

Version: 1.8

Contact: toby.hocking@inria.fr

The directlabels package provides an extensible framework for automatically placing direct labels onto multicolor lattice or ggplot2 plots. It includes heuristics for examining "lattice" and "ggplot" objects and inferring an appropriate Positioning Method for placing the labels. Furthermore, the design of directlabels makes it simple to create Positioning Methods for specific plots or libraries of portable Positioning Methods that can be re-used.

See also the web page
http://

Version: 2.2

Contact: toby.hocking@inria.fr

The clusterpath package provides an R/C++ implementation of the algorithms described in .

See also the web page
http://

Version: 1.0

Contact: toby.hocking@inria.fr

UGM is a set of Matlab functions implementing various tasks in probabilistic undirected graphical models of discrete data with pairwise (and unary) potentials. Specifically, it implements a variety of methods for the following four tasks:

Decoding: Computing the most likely configuration.

Inference: Computing the partition function and marginal probabilities.

Sampling: Generating samples from the distribution.

Parameter Estimation: Given data, computing maximum likelihood (or MAP) estimates of the parameters.

The first three tasks are implemented for arbitrary discrete undirected graphical models with pairwise potentials. The last task focuses on Markov random fields and conditional random fields with log-linear potentials. The code is written entirely in Matlab, although more efficient mex versions of some parts of the code are also available.

See also the web page
http://

Version: 2011

Contact: mark.schmidt@inria.fr

The code contains implementations of several available methods for the problem of computing an approximate minimizer of the sum of a set of unary and pairwise real-valued functions over discrete variables. This equivalent to the problem of MAP estimation, also known as decoding, in a pairwise undirected graphical model. The code focuses on scenarios where the pairwise energies encourage neighboring variables to take the same state. The particular methods contained in the package are iterated conditional mode, alpha-beta swaps, alpha-expansions, and alpha-expansion beta-shrink moves.

See also the web page
http://

Version: 1

Contact: mark.schmidt@inria.fr

This package contains the code used to produce the results in Mark Schmidt's thesis: Roughly, there are five components corresponding to five of the thesis chapters:

Chapter 2: L-BFGS methods for optimizing differentiable functions plus an L1-regularization term.

Chapter 3: L-BFGS methods for optimizing differentiable functions with simple constraints or regularizers.

Chapter 4: An L1-regularization method for learning dependency networks, and methods for structure learning in directed acyclic graphical models.

Chapter 5: L1-regularization and group L1-regularization for learning undirected graphical models, using either the L2, Linf, or nuclear norm of the groups.

Chapter 6: Overlapping group L1-regularization for learning hierarhical log-linear models, and an active set method for searching through the space of higher-order groups.

See also the web page
http://

Version: 1

Contact: mark.schmidt@inria.fr

Many structured data-fitting applications require the solution of an optimization problem involving a sum over a potentially large number of measurements. Incremental gradient algorithms (both deterministic and randomized) offer inexpensive iterations by sampling only subsets of the terms in the sum. These methods can make great progress initially, but often slow as they approach a solution. In contrast, full gradient methods achieve steady convergence at the expense of evaluating the full objective and gradient on each iteration. We explore hybrid methods that exhibit the benefits of both approaches. Rate of convergence analysis and numerical experiments illustrate the potential for the approach.

See also the web page
http://

Version: 1

Contact: mark.schmidt@inria.fr

Participants outside of Sierra: Michael Friedlander (Scientific Computing Laboratory, Department of Computer Science, University of British Columbia)

This toolbox implements statistical algorithms designed to perform multi-task kernel ridge regressions, as described in .

See also the web page
http://

Version: 1

Contact: matthieu.solnon@ens.fr

Kernel density estimation, a.k.a. Parzen windows, is a popular density estimation method, which can be used for outlier detection or clustering. With multivariate data, its performance is heavily reliant on the metric used within the kernel. Most earlier work has focused on learning only the bandwidth of the kernel (i.e., a scalar multiplicative factor). In this paper, we propose to learn a full Euclidean metric through an expectation-minimization (EM) procedure, which can be seen as an unsupervised counterpart to neighbourhood component analysis (NCA). In order to avoid overfitting with a fully nonparametric density estimator in high dimensions, we also consider a semi-parametric Gaussian-Parzen density model, where some of the variables are modelled through a jointly Gaussian density, while others are modelled through Parzen windows. For these two models, EM leads to simple closed-form updates based on matrix inversions and eigenvalue decompositions. We show empirically that our method leads to density estimators with higher test-likelihoods than natural competing methods, and that the metrics may be used within most unsupervised learning techniques that rely on such metrics, such as spectral clustering or manifold learning methods. Finally, we present a stochastic approximation scheme which allows for the use of this method in a large-scale setting .

Collaboration with: Nicolas Heess (School of Informatics, University of Edinburgh) and John Winn (Machine Learning and Perception, Microsoft research Cambridge).

We propose an extension of the Restricted Boltzmann Machine (RBM) that allows the joint shape and appearance of foreground objects in cluttered images to be modeled independently of the background. We present a learning scheme that learns this representation directly from cluttered images with only very weak supervision. The model generates plausible samples and performs foreground-background segmentation. We demonstrate that representing foreground objects independently of the background can be beneficial in recognition tasks .

Collaboration with: Jean-Philippe Vert (INSERM U900, Mines ParisTech, Institut Curie).

We present a new clustering algorithm by proposing a convex relaxation of hierarchical clustering, which results in a family of objective functions with a natural geometric interpretation. We give efficient algorithms for calculating the continuous regularization path of solutions, and discuss relative advantages of the parameters. Our method experimentally gives state-of-the-art results similar to spectral clustering for non-convex clusters, and has the added benefit of learning a tree structure from the data .

Collaboration with: Julien Mairal (Department of Statistics, University of California, Berkeley).

Collaboration with: Alexandre Gramfort, Vincent Michel, Evelyn Eger and Bertrand Thirion (Laboratoire de Neuroimagerie Assistée par Ordinateur (LNAO), CEA: DSV/I2BM/NEUROSPIN, PARIETAL (INRIA Saclay - Ile de France) and Neuroimagerie cognitive, INSERM: U992 – Université Paris Sud – CEA).

Inverse inference, or "brain reading", is a recent paradigm for analyzing functional magnetic resonance imaging (fMRI) data, based on pattern recognition and statistical learning. By predicting some cognitive variables related to brain activation maps, this approach aims at decoding brain activity. Inverse inference takes into account the multivariate information between voxels and is currently the only way to assess how precisely some cognitive information is encoded by the activity of neural populations within the whole brain. However, it relies on a prediction function that is plagued by the curse of dimensionality, since there are far more features than samples, i.e., more voxels than fMRI volumes. To address this problem, different methods have been proposed, such as, among others, univariate feature selection, feature agglomeration and regularization techniques. In this paper, we consider a sparse hierarchical structured regularization. Specifically, the penalization we use is constructed from a tree that is obtained by spatially-constrained agglomerative clustering. This approach encodes the spatial structure of the data at different scales into the regularization, which makes the overall prediction procedure more robust to inter-subject variability. The regularization used induces the selection of spatially coherent predictive brain regions simultaneously at different scales. We test our algorithm on real data acquired to study the mental representation of objects, and we show that the proposed algorithm non only delineates meaningful brain regions but yields as well better prediction accuracy than reference methods , .

Collaboration with: Karteek Alahari (Willow project-team, INRIA Paris-Rocquencourt).

Collaboration with: Michael P. Friedlander (University of British Columbia).

Many structured data-fitting applications require the solution of an optimization problem involving a sum over a potentially large number of measurements. Incremental gradient algorithms offer inexpensive iterations by sampling only subsets of the terms in the sum. These methods can make great progress initially, but often slow as they approach a solution. In contrast, full gradient methods achieve steady convergence at the expense of evaluating the full objective and gradient on each iteration. We explore hybrid methods that exhibit the benefits of both approaches. Rate of convergence analysis shows that by controlling the size of the subsets in an incremental gradient algorithm, it is possible to maintain the steady convergence rates of full gradient methods. We detail a practical quasi-Newton implementation based on this approach, and numerical experiments illustrate its potential benefits .

Collaboration with: Y-Lan Boureau and Jean Ponce (Willow project-team, INRIA Paris-Rocquencourt) and Yann LeCun (Courant Institute of Mathematical Science (CIMS), New York University).

Invariant representations in object recognition systems are generally obtained by pooling feature vectors over spatially local neighborhoods. But pooling is not local in the feature vector space, so that widely dissimilar features may be pooled together if they are in nearby locations. Recent approaches rely on sophisticated encoding methods and more specialized codebooks (or dictionaries), e.g., learned on subsets of descriptors which are close in feature space, to circumvent this problem. In this work, we argue that a common trait found in much recent work in image recognition or retrieval is that it leverages locality in feature space on top of purely spatial locality. We propose to apply this idea in its simplest form to an object recognition system based on the spatial pyramid framework, to increase the performance of small dictionaries with very little added engineering. State-of-the-art results on several object recognition benchmarks show the promise of this approach .

Using the

The concept of parsimony is central in many scientiﬁc domains. In the context of statistics, signal processing or machine learning, it may take several forms. Classically, in a variable or feature selection problem, a sparse solution with many zeros is sought so that the model is either more interpretable, cheaper to use, or simply matches available prior knowledge. In this work, we instead consider sparsity-inducing regularization terms that will lead to solutions with many equal values. A classical example is the total variation in one or two dimensions, which leads to piecewise constant solutions and can be applied to various image labelling problems, or change point detection tasks. In this work , we follow our earlier approach which consisted in designing sparsity-inducing norms based on non-decreasing submodular functions, as a convex approximation to imposing a speciﬁc prior on the supports of the predictors. Here, we show that a similar parallel holds for some other class of submodular functions, namely non-negative setfunctions which are equal to zero for the full and empty set. Our main instance of such functions are symmetric submodular functions and we show that the Lovász extension may be seen as the convex envelope of a function that depends on level sets (i.e., the set of indices whose corresponding components of the underlying predictor are greater than a given constant). By selecting speciﬁc submodular functions, we give a new interpretation to known norms, such as the total variation; we also deﬁne new norms, in particular ones that are based on order statistics with application to clustering and outlier detection, and on noisy cuts in graphs with application to change point detection in the presence of outliers.

Collaboration with: Cédric Févotte (Laboratoire traitement et communication de l'information (LTCI), CNRS: UMR5141 – Institut Télécom – Télécom ParisTech).

Collaboration with: Cédric Févotte (Laboratoire traitement et communication de l'information (LTCI), CNRS: UMR5141 – Institut Télécom – Télécom ParisTech).

Nonnegative matrix factorization (NMF) is now a common tool for audio source separation. When learning NMF on large audio databases, one major drawback is that the complexity in time is O(FKN) when updating the dictionary (where (F;N) is the dimension of the input power spectrograms, and K the number of basis spectra), thus forbidding its application on signals longer than an hour. We provide an online algorithm with a complexity of O(FK) in time and memory for updates in the dictionary. We show on audio simulations that the online approach is faster for short audio signals and allows to analyze audio signals of several hours .

Collaboration with: Olivier Duchenne and Jean Ponce (Willow project-team, INRIA Paris-Rocquencourt).

Collaboration with: Laurent Jacob (Department of Statistics, University of California at Berkeley) and Jean-Philippe Vert (INSERM U900, Mines ParisTech, Institut Curie).

Collaboration with: Julien Mairal (Department of Statistics, University of California, Berkeley) and Jean Ponce (Willow project-team, INRIA Paris-Rocquencourt).

Sparse coding, which is the decomposition of a vector using only a few basis elements, is widely used in machine learning and image processing. The basis set, also called dictionary, is learned to adapt to specific data. This approach has proven to be very effective in many image processing tasks. Traditionally, the dictionary is an unstructured "flat" set of atoms. In this work, we study structured dictionaries which are obtained from an epitome, or a set of epitomes. The epitome is itself a small image, and the atoms are all the patches of a chosen size inside this image. This considerably reduces the number of parameters to learn and provides sparse image decompositions with shift invariance properties. We propose a new formulation and an algorithm for learning the structured dictionaries associated with epitomes, and illustrate their use in image denoising tasks. This work has resulted in a CVPR'11 publication .

Collaboration with: Julien Mairal (Department of Statistics, University of California, Berkeley) and Jean Ponce (Willow project-team, INRIA Paris-Rocquencourt).

The paper proposes a novel approach to image deblurring and digital zooming using sparse local models of image appearance. These models, where small image patches are represented as linear combinations of a few elements drawn from some large set (dictionary) of candidates, have proven well adapted to several image restoration tasks. A key to their success has been to learn dictionaries adapted to the reconstruction of small image patches . In contrast, recent works have proposed instead to learn dictionaries which are not only adapted to data reconstruction, but also tuned for a specific task . We introduce here such an approach to deblurring and digital zoom, using pairs of blurry/sharp (or low-/high-resolution) images for training, as well as an effective stochastic gradient algorithm for solving the corresponding optimization task. Although this learning problem is not convex, once the dictionaries have been learned, the sharp/high-resolution image can be recovered via convex optimization at test time. Experiments with synthetic and real data demonstrate the effectiveness of the proposed approach, leading to state-of-the-art performance for non-blind image deblurring and digital zoom.

Collaboration with: Olivier Catoni (École Normale Supérieure, CNRS and INRIA Paris-Rocquencourt, Classic project-team)

Collaboration with: Anne-Marie Tousch (École des Ponts and ONERA) and Stéphane Herbin (ONERA)

Collaboration with: Antoine Salomon (École des Ponts)

Collaboration with: Sébastien Bubeck (Centre de Recerca Matematica of Barcelona) and Gabor Lugosi (ICREA and Pompeu Fabra University)

Collaboration with: Eric Moulines (Telecom ParisTech)

Title: New statistical approaches to computer vision and bioinformatics

Coordinator: École Normale Supérieure (Paris)

Leader of the project: Sylvain Arlot

Other members: J. Sivic (Willow project-team, ENS), A. Celisse (University Lille 1), T. Mary-Huard (AgroParisTech), E. Roquain and F. Villers (University Paris 6).

Instrument: ANR “Young researchers” Program

Duration: Sep 2009 - Aug 2012

Total funding: 70000 Euros

Abstract: The Detectproject aims at providing new statistical approaches for detection problems in computer vision (in particular, detecting and recognizing human actions in videos) and bioinformatics (e.g., simultaneously segmenting CGH profiles). These problems are mainly of two different statistical nature: multiple change-point detection (i.e., partitioning a sequence of observations into homogeneous contiguous segments) and multiple tests (i.e., controlling a priori the number of false positives among a large number of tests run simultaneously).

Title: SIERRA – Sparse structured methods for machine learning

Type: IDEAS

Instrument: ERC Starting Grant

Duration: December 2009 - November 2014

Coordinator: INRIA (France)

See also:
http://

Abstract: Machine learning is now a core part of many research domains, where the abundance of data has forced researchers to rely on automated processing of
information. The main current paradigm of application of machine learning techniques consists in two sequential stages: in the
*representation*phase, practitioners first build a large set of features and potential responses for model building or prediction. Then, in the
*learning*phase, off-the-shelf algorithms are used to solve the appropriate data processing tasks.

While this has led to significant advances in many domains, the potential of machine learning techniques is far from being reached: the tenet of this proposal is that to achieve the
expected breakthroughs, this two-stage paradigm should be replaced by an integrated process where the specific
*structure*of a problem is taken into account explicitly in the learning process. Considering such structure appropriately allows the consideration of massive numbers of features
or potentially the on-demand construction of relevant features, in both numerically efficient and theoretically understood ways. Thus, one could get the benefits of very large numbers
of features—e.g., better predictive performance—in a reasonable running time.

This problem will be attacked through the tools of regularization by
*sparsity-inducing norms*, that have recently led to theoretical and algorithmic advances, as well as practical successes, in unstructured domains. The scientific objective is thus
to
*marry structure with sparsity*: this is particularly challenging because structure may occur in various ways (discrete, continuous or mixed) and the targeted applications in
computer vision and audio processing lead to large-scale convex optimization problems.

Title: Fast Statistical Analysis of Web Data via Sparse Learning

INRIA principal investigator: Francis Bach

International Partner:

Institution: University of California Berkeley (United States)

Laboratory: EECS and IEOR Departments

Duration: 2011 - 2013

The goal of the proposed research is to provide web-based tools for the analysis and visualization of large corpora of text documents, with a focus on databases of news articles. We intend to use advanced algorithms, drawing from recent progresses in machine learning and statistics, to allow a user to quickly produce a short summary and associated timeline showing how a certain topic is described in news media. We are also interested in unsupervised learning techniques that allow a user to understand the difference between several different news sources, topics or documents.

F. Bach: Banff workshop on Sparse Statistics, Optimization and Machine Learning, January 2011. Co-organizer.

N. Le Roux: NIPS Workshop on Deep Learning and Unsupervised Feature Learning, December 2011. Co-organizer.

F. Bach, G. Obozinski: ICML 2011 workshop "Structured Sparsity: Learning and Inference Workshop", July 2nd 2011, Bellevue, Washington, USA. Co-organizers.

F. Bach, G. Obozinski: NIPS 2011 workshop Sparse Representation and Low-rank approximation, December 16th 2011, Sierra Nevada, Spain. Co-organizers.

F. Bach: Journal of Machine Learning Research, Action Editor

F. Bach: IEEE Transactions on Pattern Analysis and Machine Intelligence, Associate Editor

F. Bach: SIAM Journal on Imaging Science, Associate Editor

J.-Y. Audibert: Conference on Learning Theory (C0LT), 2011

J.-Y. Audibert: International Joint Conference on Artificial Intelligence (IJCAI), 2011

J.-Y. Audibert: Algorithmic Learning Theory (ALT), 2011

F. Bach: International Conference on Machine Learning, 2011, Area chair, Workshop co-chair

F. Bach: International Conference on Computer Vision, 2011, Area chair

G. Obozinski: Fifteenth International Conference on Artificial Intelligence and Statistics

International journals: Annals of Statistics, Artificial Intelligence , Computational Statistics and Data Analysis (CSDA), Electronic Journal of Statistics (EJS), IEEE Transactions on Information theory, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), IEEE Transaction on Signal Processing, Journal of Computational and Graphical Statistics (JCGS), Journal of Machine Learning Research (JMLR), Journal of the Royal Statistical Society (JRSS), Machine Learning, Signal Processing Elsevier, Statistics and Computing (STCO)

International conferences: AISTATS, ALT, ICML, ICPRAM, NIPS

Pierre Connault, University Paris-Sud, 2011 (S. Arlot)

Rodolphe Jenatton, ENS Cachan, 2011 (J.-Y. Audibert, F. Bach, G. Obozinski)

Jean-Baptiste Monnier, Université Paris 7, 2011 (J.-Y. Audibert, F. Bach)

Novi Quadrianto, Australian National University (F. Bach)

Gilles Meyer, Université de liège (F. Bach)

S. Arlot is member of the board for the entrance exam in École Normale Supérieure (mathematics, voie B/L)

J.-Y. Audibert, F. Bach, Co-organizers of the biweekly seminar “Statistical Machine Learning in Paris" (
http://

CNRS Prime d'excellence scientifique (S. Arlot)

S. Arlot: Ph.D. thesis prize from SFDS (Prix Marie-Jeanne Laurent-Duhamel 2011)

T. Hocking: Best Student Poster at useR 2011 in Warwick, England for “Adding direct labels to plots”

M. Schmidt: NSERC posdoctoral fellowship

S. Arlot, “Sélection d'estimateurs avec des pénalités aléatoires”, lecture for the Marie-Jeanne Laurent-Duhamel prize, 43e Journées de Statistique (Société Française de Statistique), Tunis, 2011.

S. Arlot, “Data-driven calibration of linear estimators with minimal penalties, with an application to multi-task regression”, Seminar, Statistics Laboratory, University of Cambridge, 2011.

S. Arlot, “Pénalités minimales et sélection d'estimateurs optimale”, Seminar, Colloquium du MAP 5, Paris 5, 2011.

S. Arlot, “Calibration automatique d'estimateurs linéaires à l'aide de pénalités minimales, application à la régression multi-tâches”, Seminar, IRMAR, Rennes, 2011.

J.-Y. Audibert, “Aggregation and robust estimation”, Seminar, Université de Lille, 2011.

J.-Y. Audibert, “Bandits Adversarials: Équilibres de Nash Approchés”, Journée “Jeux Matriciels et Jeux Partiellement Observables”, Paris, 2011.

J.-Y. Audibert, “Introduction to Bandits: Algorithms and Theory” (in collaboration with Rémi Munos, INRIA Sequel), ICML tutorial, Bellevue, United States, 2011.

F. Bach, Small Workshop on Sparse Dictionary Learning (invited talk) - Queen Mary University, London - January 2011

F. Bach, Ecole Polytehnique Fédérale de Lausanne (seminar) - February 2011

F. Bach, Statlearn workshop on Challenging problems in Statistical Learning (invited talk) - Grenoble - March 2011

F. Bach, Learning Theory/State of the Art (invited talk) - Institut Henri Poincaré, Paris - May 2011

F. Bach, Congrès SMAI 2011 (invited talk in special session) - Lorient - May 2011

F. Bach, Université de Lille (seminar) - June 2011

F. Bach, Workshop on Signal Processing with Adaptive Sparse Structured Representations (invited talk) - Edimburg - June 2011

F. Bach, Conference on Foundations of Computational Mathematics (two invited talks) - Budapest - July 2011

F. Bach, Université de Liège (seminar) - September 2011

F. Bach, IMA Workshop on High Dimensional Phenomena (invited tutorial) - Minneapolis - September 2011

F. Bach, University of California, Berkeley (two seminars) - October 2011

F. Bach, Machine Learning for NeuroImaging Workshop (invited talk) - November 2011

F. Bach, Rencontres de Statistique Mathématique (invited talk) - Luminy - December 2011

F. Bach, NIPS workshop on Discrete Optimization (invited talk) - Granada - December 2011

F. Bach, NIPS workshop on Deep Learning (invited talk) - Granada - December 2011

T. Hocking, “Collaborative R package development using R-Forge” and “Sustainable R package development using documentation generation”, Seminar on bioinformatics, Institut de Biologie de Lille, 9 June 2011.

G. Obozinski, “Structured Sparse Coding: Efficient algorithms and applications”, invited talk, BIRS workshop "Sparse Statistics, Optimization and Machine Learning", Banff, January 2011

G. Obozinski, “Convex relaxation for Combinatorial Penalties”, invited talk, Mini-workshop on "Mathematical and Computational Foundations of Learning Theory", Dagstuhl, July 2011

G. Obozinski, “Convex relaxation for Combinatorial Penalties”, invited talk, Mini-Workshop: "Mathematics of Machine Learning", Oberwolfach, August 2011

G. Obozinski, “Introduction to Statistical Learning”, tutorial talk, GDR Mascot-Num session "Statistical learning for computer experiments", Institut Henri Poincaré, May 2011

M. Schmidt, “Structure Learning in Undirected Graphical Models”, Invited lecture at INRA workshop on network inference, Toulouse, January 2011.

M. Schmidt, “Hybrid Deterministic-Stochastic Methods for Data Fitting”, Invited talk at Xerox Research Centre, Grenoble, July 2011.

M. Schmidt, “Convex Optimization”, Practical session at MLSS 2011, Bordeaux, September 2011.

M. Schmidt, “Inexact Gradient and Proximal-Gradient Methods”, Invited lecture at EPFL, November 2011.

M. Solnon, “Multi-task ridge regression using minimal penalties”, Séminaire de l'unité MIA-Jouy, Jouy en Josas, France, November 2011.

Licence:

S. Arlot, J.-Y. Audibert, F. Bach and G. Obozinski, “Statistical learning”, 30h, L3, École Normale Supérieure (Paris), France.

Master:

S. Arlot, “Leçons de Mathématiques: Classification”, 9h, M1, École Normale Supérieure (Paris), France

S. Arlot and Francis Bach, “Statistical learning”, 24h, M2, Université Paris-Sud, France.

J.-Y. Audibert, “Machine Learning and applications”, 30h, M2, École des Ponts ParisTech and Université Paris-Est Marne-la-Vallée, France

J.-Y. Audibert, “Prédiction séquentielle”,14h, M2, Université Paris 7, France

F. Bach and G. Obozinski, “Probabilistic Graphical Models”, 30h, M2, Mastère MVA, ENS Cachan, France.

N. Le Roux, “Neural Networks and Optimization Methods”, 3h, M2, Mastère MVA, ENS Cachan, France.

G. Obozinski, Enseignement spécialisé “Apprentissage Artificiel”, 3h, M1 (Graduate 1st year level), Mines de Paris, April 29th 2011.

Doctorat:

S. Arlot, “Sélection de modèles et sélection d'estimateurs pour l'Apprentissage statistique”, 8h, Collège de France, France

S. Arlot, “Model selection and estimator selection for statistical learning”, 10h, Scuola Normale Superiore di Pisa, Italy

G. Obozinski, Summer school on Sparsity and Model Selection, 10h, Centro de Matematica Montevideo Urugaya, Montevideo, Uruguay, Feb. 28 - March 4, 2011.

PhD & HdR:

PhD: Rodolphe Jenatton, “Normes Parcimonieuses Structurées: Propriétés Statistiques et Algorithmiques avec Applications à l'Imagerie Cérébrale”, ENS Cachan, November 2011, J.-Y. Audibert and F. Bach.

PhD in progress:

Louise Benoît, 2009, F. Bach and J. Ponce

Florent Couzinie-Devy, 2010, F. Bach and J. Ponce

Edouard Grave, 2010, F. Bach and G. Obozinski

Toby Hocking, 2009, F. Bach and Jean-Philippe Vert (Ecole des Mines de Paris)

Armand Joulin, 2009, F. Bach and J. Ponce

Augustin Lefèvre, 2009, F. Bach and Cédric Févotte (Telecom Paristech)

Anil Nelakanti, 2010, Cédric Archambeau (Xerox) and F. Bach

Fabian Pedregosa, 2011, F. Bach and Alexandre Gramfort (INRIA Saclay)

Matthieu Solnon, Multi-task learning, September 2010, S. Arlot and F. Bach