Zoltán Szabó: machine learning journal club @ CMAP, mini courses / external talks (Chair Stress Test; 2018, Nov.-). The event takes place every 2nd week (2020, Apr. 23-) in turn with the SIMPAS group meeting.

Date Presenter Title Paper
Apr. 15 Margaux Zaffran Conformalized Quantile Regression paper
Apr. 1 Costanza Tortú Machine learning algorithms tailored for causal inference studies
Mar. 18 Marc Chataigner Nowcasting Networks paper
Feb. 18 Charu Shardul A Reinforcement Learning approach for mean-variance portfolio selection problem link1, link2
Feb. 4 David Métivier A mean field view of the landscape of two-layer neural networks paper

Date Presenter Title Paper
Nov. 26 Bouazza Saadeddine Deep XVAs
Nov. 12 Linda Chamakh Statistical Optimal Transport posed as Learning Kernel Mean Embedding paper
Oct. 15 Costanza Tortú Heterogeneous Treatment and Spillover Effects Under Clustered Network Interference paper
July 23 Linda Chamakh Portfolio Optimization: Distribution Targeting with Kernel-based Divergence
July 9 David Barrera Efficient Parameter Estimation of Sampled Random Fields paper
June 25 Bouazza Saadeddine Probabilistic Line Searches for Stochastic Optimization paper
June 11 Marc Chataigner Deep Local Volatility paper
May 14 Wei Jiang Integrating multi-source block-wise missing data in model selection paper
Mar. 5 Zoltán Szabó (me) Shape-Constrained Estimation in Reproducing Kernel Hilbert Spaces paper
Apr. 30 Michael Allouche Variational Autoencoder: a Bayesian Approach for Generative Models with Neural Networks paper
Apr. 16 Maud Thomas (external seminar) Concentration Results in Extreme Value Theory
Apr. 9 Bouazza Saadeddine Gradient Boosting Neural Networks: GrowNet paper
Apr. 2 Anne Sabourin (external talk) Exploratory analysis for high dimensional extremes: Support identification, Anomaly detection, Principal component analysis
Mar. 19 Chiara Amorino (external seminar) Invariant adaptive density estimation for ergodic SDE with jumps over anisotropic classes
Mar. 12 Hideatsu Tsukahara (external talk) Mini course on 'Statistics of Risk Measures: Estimation and Backtesting'
Mar. 9 Hideatsu Tsukahara (external talk) A copula approach to spatial econometrics with applications to finance
Feb. 27 Dorinel Bastide (external talk) Calculation of Credit Provision to CCP Exposures Using Expected Loss Approach
Feb. 27 Cyril Benezet Deep Learning Approximations of Stochastic Control Problems and PDEs in High Dimension paper (link-1), paper (link-2)
Feb. 13 James Brice Scoggins Uniformly accurate machine learning-based hydrodynamic models for kinetic equations paper (link-1), paper (link-2)
Jan. 30 Bruno Loureiro (external talk) Are Generative Models the New Sparsity?

Date Presenter Title Paper
Nov. 28 Siragan Gailus (external talk) Homogenization and Fluctuations for Diffusions with Standard and Fractional Brownian Motion
Nov. 28 Linda Chamakh How Many Samples are Needed to Estimate a Convolutional or Recurrent Neural Network? paper
Nov. 21 Jerome Stenger (external talk) Optimal Uncertainty Quantification of a Risk Measurement on a Moment Class
Nov. 21 Alex Lambert Learning function-valued functions in RKHSs: application to integral losses
Nov. 7 Gwladys Toulemonde (external talk) Stochastic modelling and simulation of extreme rainfalls
Nov. 7 Bouazza Saadeddine Multi-Task Learning as Multi-Objective Optimization paper
Oct. 24 Rodrigo Targino (external talk) Understanding the Economic Policy Uncertainty Index Using Semi-Automatic News Classification
Oct. 24 Michael Allouche Enriching Financial Datasets with GAN paper
Oct. 17 Rodrigo Targino (external talk) Elements of risk management and the allocation problem
Oct. 10 Pierre-Cyril Aubin-Frankowski Finding Mixed Nash Equilibria of Generative Adversarial Networks paper, supplement
Oct. 3 Philippe Durand (external talk) Financial Crisis and its Evaluation
June 13 Clément Dombry (external talk) The coupling method in extreme value theory
June 13 Imke Mayer: homepage, LinkedIn Invariant Causal Prediction for Nonlinear Models paper
June 6 Marcos Carreira Learning Interest Rate Interpolation
May 16 Kaitong Hu Mean field Langevin dynamic and its applications to neural network
May 9 Antoine Usseglio-Carleve (external talk) Estimation of conditional extreme risk measures from heavy-tailed elliptical random vector paper
May 9 Linda Chamakh On the Margin Theory of Feedforward Neural Networks paper
Apr. 24 & 25 Jean-David Fermanian (external talk) Copulas
Apr. 4 Arnaud Guyader (external talk) Introduction to rare event simulation methods
Apr. 4 Bouazza Saadeddine Online natural gradient as a Kalman filter paper
Mar. 28 Frédéric Loge Munerel Deep Knockoffs paper
Mar. 21 Gildas Mazo (external talk) An optimal balance between explorations and repetitions in sensitivity analysis
Mar. 14 Marc Chataigner How well can generative adversarial network learn densities : A nonparametric view paper
Mar. 7 Guillaume Perrin (external talk) Exploiting code structure for statistical learning
Mar. 7 Aude Sportisse Variational Inference for Stochastic Block Models from Sampled Data paper
Feb. 21 James Brice Scoggins The Power of Deeper Networks for Expressing Natural Functions paper
Feb. 14 Wei Jiang A Modern Maximum-Likelihood Theory for High-dimensional Logistic Regression paper
Feb. 7 Anne Sabourin (external talk) Binary Classification in Extreme Regions paper
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 talk) Mini course on 'Introduction to extreme-value analysis' (part-3)

Date Presenter Title Paper
Dec. 13 Stéphane Girard (external talk) Mini course on 'Introduction to extreme-value analysis' (part-2)
Dec. 13 Imke Mayer: LinkedIn, homepage Structured Low-Rank Matrix Factorization: Global Optimality, Algorithms, and Applications paper, supplement
Dec. 6 Rémi Besson Safe and Efficient Off-Policy Reinforcement Learning paper
Nov. 29 Stéphane Girard (external talk) Mini course on 'Introduction to extreme-value analysis' (part-1)
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 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
Mar. 15 Cédric Rommel Soft-DTW: a Differentiable Loss Function for Time-Series paper
Mar. 8 Belhal Karimi A Universal Catalyst for First-Order Optimization paper
Feb. 22 Manon Michel Understanding deep learning requires rethinking generalization paper

Date Presenter Title Paper
Nov. 23 Alain Virouleau Online rules for control of False Discovery Rate and False Discovery Exceedance link
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 paper
Oct. 26 Martin Royer Adaptive Clustering through Semidefinite Programming (NIPS preview) link
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
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)
Sept. 21 Marcos Carreira Discovering Latent Network Structure in Point Process Data link
July 6 Bharath Sriperumbudur (external talk) Statistical Consistency of Kernel PCA with Random Features link
June 29 Frédéric Loge Munerel The statistical performance of collaborative inference link
June 22 Barnabás Póczos (external talk) Density Functional Estimation
June 8 Martin Bompaire (on LinkedIn, ResearchGate) ASAGA: Asynchronous Parallel SAGA paper, supplement
June 1 Wei Jiang Post-selection inference for l1-penalized likelihood models link-1, link-2 (arXiv)
May 23 Gustaw Matulewicz Lasso, fractional norm and structured sparse estimation using a Hadamard product parametrization. link
May 18 Massil Achab (on LinkedIn, ResearchGate) Operator Variational Inference link
May 11 David Barrera Online Learning with Markov Sampling. link
May 4 Zoltán Szabó (me) Examples are not enough, learn to criticize! Criticism for Interpretability link
Apr. 27 Kirthevasan Kandasamy (external talk) Bandit Optimisation with Approximations link1, link2, link3
Apr. 13 Nicolas Brosse Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling link
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
Mar. 9 Geneviève Robin Random matrix theory in statistics: A review link
Mar. 2 Joon Kwon An Ultimate Unification of Gradient and Mirror Descent (aka "Nesterov's acceleration: the simplest proof ever") link
Feb. 23 Cédric Rommel A Consistent Regularization Approach for Structured Prediction link
Feb. 16 Belhal Karimi Consistent Kernel Mean Estimation for Functions of Random Variables link