Zoltán Szabó: machine learning journal club (=:MLJC) @ CMAP (co-organizer, 2018-: Elodie Vernet) & mini courses / external seminars (Chair Stress Test, headed by Emmanuel Gobet, 2018 Nov.-).

2019
Date Presenter Title Paper Slides
Apr. 4 Bouazza Saadeddine
Mar. 28 Frédéric Loge Munerel
Mar. 21 Juliette Chevallier
Mar. 14 Marc Chataigner
Mar. 7 Antoine Usseglio (external seminar) Estimation of conditional extreme risk measures from heavy-tailed elliptical random vector abstract
Mar. 7 Nicolas Prost
Feb. 21 James Brice Soggins
Feb. 14 Wei Jiang A Modern Maximum-Likelihood Theory for High-dimensional Logistic Regression paper slides
Feb. 7 Anne Sabourin (external seminar) Binary Classification in Extreme Regions abstract, paper slides
Jan. 31 David Barrera Two strong consistency theorems for nonparametric regression over independent samplings paper
Jan. 24 Geneviève Robin Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues paper
Jan. 10 Stéphane Girard (external seminar) Mini course on 'Introduction to extreme-value analysis' (part-3) abstract slides

2018
Date Presenter Title Paper Slides
Dec. 13 Stéphane Girard (external seminar) Mini course on 'Introduction to extreme-value analysis' (part-2) abstract slides-1, slides-2
Dec. 13 Imke Mayer: LinkedIn, homepage Structured Low-Rank Matrix Factorization: Global Optimality, Algorithms, and Applications paper, supplement slides
Dec. 6 Rémi Besson Safe and Efficient Off-Policy Reinforcement Learning paper
Nov. 29 Stéphane Girard (external seminar) Mini course on 'Introduction to extreme-value analysis' (part-1) abstract slides-1, slides-2
Nov. 29 Nicolas Prost GAIN: Missing Data Imputation using Generative Adversarial Nets paper
Nov. 22 Kaitong Hu Mean Field Analysis of Neural Networks paper
Nov. 8 Othmane Mounjid Gradient Descent Provably Optimizes Over-parameterized Neural Networks paper
Oct. 25 Aude Sportisse Why are Big Data Matrices Approximately Low Rank? paper
Oct. 18 Frédéric Loge Munerel Bias and variance approximation in value function estimates paper
Oct. 11 Giulio Gori New Approaches in Turbulence and Transition Modeling Using Data-driven Techniques paper
Sept. 27 Francois Sanson Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling paper, DOI
June 14 Juliette Chevallier A Bayesian mixed-effects model to learn trajectories of changes for repeated manifold-valued observations paper
June 7 Frédéric Loge Munerel A Distributional Perspective on Reinforcement Learning paper, supplement
May 24 Jaouad Mourtada Progressive mixture rule are deviation suboptimal paper
Apr. 26 Geneviève Robin Optimal algorithms for smooth and strongly convex distributed optimization in networks paper, supplement
Apr. 5 Maryan Morel Determinantal point processes for machine learning paper slides
Mar. 15 Cédric Rommel Soft-DTW: a Differentiable Loss Function for Time-Series paper slides
Mar. 8 Belhal Karimi A Universal Catalyst for First-Order Optimization paper slides
Feb. 22 Manon Michel Understanding deep learning requires rethinking generalization paper slides

2017
Date Presenter Title Paper Slides
Nov. 23 Alain Virouleau Online rules for control of False Discovery Rate and False Discovery Exceedance link slides
Nov. 16 Othmane Mounjid Rainbow: Combining Improvements in Deep Reinforcement Learning link
Nov. 9 Kaitong Hu Solving Imperfect Information Games Using Decomposition. Regret Minimization in Games with Incomplete Information link-1, link-2 slides
Oct. 26 Martin Royer Adaptive Clustering through Semidefinite Programming (NIPS preview) link slides
Oct. 19 Gaspar Massiot Influence Function and Robust Variant of Kernel Canonical Correlation Analysis link
Oct. 12 Rémi Besson Failures of Gradient-Based Deep Learning link-1, link-2 (authors') slides
Oct. 5 Elodie Vernet Uncertainty Quantification for the Horseshoe link
Sept. 28 Wei Jiang The Stochastic Topic Block Model for the Clustering of Vertices in Networks with Textual Edges link-1, link-2 (arXiv) slides
Sept. 21 Marcos Carreira Discovering Latent Network Structure in Point Process Data link slides
July 6 Bharath Sriperumbudur (external seminar) Statistical Consistency of Kernel PCA with Random Features link slides
June 29 Frédéric Loge Munerel The statistical performance of collaborative inference link
June 22 Barnabás Póczos (external seminar) Density Functional Estimation slides
June 8 Martin Bompaire (on LinkedIn, ResearchGate) ASAGA: Asynchronous Parallel SAGA main, supplement
June 1 Wei Jiang Post-selection inference for l1-penalized likelihood models link-1, link-2 (arXiv) handout
May 23 Gustaw Matulewicz Lasso, fractional norm and structured sparse estimation using a Hadamard product parametrization. link handout
May 18 Massil Achab (on LinkedIn, ResearchGate) Operator Variational Inference link handout
May 11 David Barrera Online Learning with Markov Sampling. link handout
May 4 Zoltán Szabó Examples are not enough, learn to criticize! Criticism for Interpretability link slides
Apr. 27 Kirthevasan Kandasamy (external seminar) Bandit Optimisation with Approximations link1, link2, link3 slides
Apr. 13 Nicolas Brosse Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling link handout
Apr. 6 Joon Kwon Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization link
Mar. 23 Alain Virouleau Panning for gold: Model-free knockoffs for High-dimensional controlled variable selection link
Mar. 16 Jaouad Mourtada Second-Order Quantile Methods for Experts and Combinatorial Games link handout
Mar. 9 Geneviève Robin Random matrix theory in statistics: A review link handout
Mar. 2 Joon Kwon An Ultimate Unification of Gradient and Mirror Descent (aka "Nesterov's acceleration: the simplest proof ever") link handout
Feb. 23 Cédric Rommel A Consistent Regularization Approach for Structured Prediction link slides
Feb. 16 Belhal Karimi Consistent Kernel Mean Estimation for Functions of Random Variables link slides

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