Program

Week Sun. 16 Mon. 17 Tue. 18 Wed. 19 Thu. 20 Fri. 21 List

	Sun. 16	Mon. 17	Tue. 18	Wed. 19	Thu. 20	Fri. 21
07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00	Arrivals 18:00 - 19:00 (1h) Arrivals Check-in at Paul Lagrange center Dinner 19:30 - 20:30 (1h) Dinner	Breakfast 7:30 - 8:30 (1h) Breakfast Welcome speech 9:15 - 9:45 (30min) Welcome speech The Marauders Map of adversarial examples: State of knowledge on evasion attacks in machine learning (1/3) 9:45 - 10:45 (1h) The Marauders Map of adversarial examples: State of knowledge on evasion attacks in machine learning (1/3) Conference room Rafael Pinot Coffee break 10:45 - 11:15 (30min) Coffee break Convex Optimization and First-Order Algorithms for Data Science (1/3) 11:15 - 12:15 (1h) Convex Optimization and First-Order Algorithms for Data Science (1/3) Conference room Adrien Taylor Lunch 12:30 - 13:30 (1h) Lunch Free time 13:30 - 15:55 (2h25) Free time Approximating Optimal Transport plans with Sliced-Wasserstein 15:55 - 16:45 (50min) Approximating Optimal Transport plans with Sliced-Wasserstein Conference room Laetitia Chapel Coffee break 16:45 - 17:15 (30min) Coffee break Speed presentation from participants 17:15 - 17:45 (30min) Speed presentation from participants Conference room CT - Session 1 17:45 - 19:00 (1h15) CT - Session 1 Conference room › Continuous sparse estimation of a Gaussian Mixture Model with unknown covariances - Romane Giard, Institut Camille Jordan 17:45-18:10 (25min) › UNHaP : Unmixing Noise from Hawkes Processes - Virginie Loison, Modèles et inférence pour les données de Neuroimagerie, Marqueurs cardiovasculaires en situation de stress 18:10-18:35 (25min) › Optimal transport with Heterogenously Missing Data - Linus Bleistein, Inria de Paris, Artificial Intelligence Laboratory [EPFL], Université Paris Dauphine-PSL 18:35-19:00 (25min) Dinner 19:30 - 20:30 (1h) Dinner	Breakfast 7:30 - 8:30 (1h) Breakfast The Marauders Map of adversarial examples: State of knowledge on evasion attacks in machine learning (2/3) 8:45 - 9:45 (1h) The Marauders Map of adversarial examples: State of knowledge on evasion attacks in machine learning (2/3) Conference room Rafael Pinot Convex Optimization and First-Order Algorithms for Data Science (2/3) 9:45 - 10:45 (1h) Convex Optimization and First-Order Algorithms for Data Science (2/3) Adrien Taylor Coffee break 10:45 - 11:15 (30min) Coffee break Transport based generative modeling and applications to sampling (1/3) 11:15 - 12:15 (1h) Transport based generative modeling and applications to sampling (1/3) Conference room Marylou Gabrié Lunch 12:30 - 13:30 (1h) Lunch Free time 13:30 - 15:30 (2h) Free time CT - Session 2 15:30 - 16:45 (1h15) CT - Session 2 Conference room › When to Forget? Complexity Trade-offs in Machine Unlearning - Martin Van Waerebeke, Centre Inria de Paris 15:30-15:55 (25min) › Consensus Monte Carlo for mixtures of categorical distributions - Julie Fendler, MRC Biostatistics Unit, University of Cambridge 15:55-16:20 (25min) › Finite-Sample Convergence Bounds for Trust Region Policy Optimization in Mean-Field Games - Antonio Ocello, ENSAE 16:20-16:45 (25min) Coffee break 16:45 - 17:15 (30min) Coffee break Optimization bilevel et unrolling 17:15 - 18:05 (50min) Optimization bilevel et unrolling Conference room Thomas Moreau Machine learning and numerical simulations. Interplay between data and physical models. 18:10 - 19:00 (50min) Machine learning and numerical simulations. Interplay between data and physical models. Conference room Mathilde Mougeot Dinner 19:30 - 20:30 (1h) Dinner	Breakfast 7:30 - 8:30 (1h) Breakfast The Marauders Map of adversarial examples: State of knowledge on evasion attacks in machine learning (3/3) 8:45 - 9:45 (1h) The Marauders Map of adversarial examples: State of knowledge on evasion attacks in machine learning (3/3) Conference room Rafael Pinot Convex Optimization and First-Order Algorithms for Data Science (3/3) 9:45 - 10:45 (1h) Convex Optimization and First-Order Algorithms for Data Science (3/3) Conference room Adrien Taylor Coffee break 10:45 - 11:15 (30min) Coffee break Conformal prediction - from a general introduction towards conditional guarantees (1/3) 11:15 - 12:15 (1h) Conformal prediction - from a general introduction towards conditional guarantees (1/3) Conference room Aymeric Dieuleveut Lunch 12:30 - 13:30 (1h) Lunch Free time 13:30 - 15:30 (2h) Free time CT - Session 3 15:30 - 16:45 (1h15) CT - Session 3 › Approche bayésienne pour évaluer l'incertitude et la robustesse d'indicateurs de graphes en neurosciences - Alice Chevaux, INRIA 15:30-15:55 (25min) › Iterated forward scheme to construct proposals for sequential Monte Carlo algorithms - Sylvain Procope-Mamert, Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] 15:55-16:20 (25min) › PAC-Bayesian Reconstruction Guarantees for Variational Autoencoders for Time Series - Chloé Hashimoto-Cullen, Laboratoire de Probabilités, Statistique et Modélisation 16:20-16:45 (25min) Poster session 17:00 - 19:15 (2h15) Poster session › Benign landscape for Burer-Monteiro factorizations of MaxCut-type semidefinite programs - Faniriana Rakoto Endor, CEntre de REcherches en MAthématiques de la DEcision 17:00-19:15 (2h15) › Validation of Computer Codes via Mixture Model Estimation - Negar Soleimani, AgroParisTech 17:00-19:15 (2h15) › Variational Inference with Mixture of Isotropic Gaussians - Marguerite Petit Talamon, Centre de Recherche en Économie et Statistique 17:00-19:15 (2h15) › Statistical and Geometrical properties of regularized Kernel Kullback-Leibler divergence - Clémentine Chazal, Centre de Recherche en Économie et Statistique 17:00-19:15 (2h15) › Gradient-free variational inference within exponential families using least squares regression with applications on discrete distributions and likelihood-free models - Le Fay Yvan, Centre de Recherche en Économie et Statistique 17:00-19:15 (2h15) › Understanding the bias induced by Local Differential Privacy - Jean Dufraiche, MAGNET (Machine Learning in Information Networks) 17:00-19:15 (2h15) › TAMIS: Tailored Membership Inference Attacks on Synthetic Data - Paul Andrey, MAGNET (Machine Learning in Information Networks) 17:00-19:15 (2h15) › Growing Neural Networks: Spotting Expressivity Bottlenecks and Fixing Them Optimally - Théo Rudkiewicz, TAU team 17:00-19:15 (2h15) › Joint estimation of bipartite network collections. Application to plant-pollinator networks - Louis LACOSTE, Mathématiques et Informatique Appliquées 17:00-19:15 (2h15) › Harnessing Mixed Features for Imbalance Data Oversampling: Application to Bank Customers Scoring - Abdoulaye SAKHO, Laboratoire de Probabilités, Statistique et Modélisation, Artefact [Paris] 17:00-19:15 (2h15) › Hallucinations in statistical inverse problems: discussion, characterization and exploration in PET imaging - Ramy Merabet, Centre Borelli 17:00-19:15 (2h15) › Streaming Federated Learning with Markovian Data - Tan-Khiem HUYNH, Centre Inria de Lyon 17:00-19:15 (2h15) › Mask-Conditional Coverage for General Missing Data Mechanisms - Jiarong FAN, Université Paris Saclay 17:00-19:15 (2h15) › Weakly supervised learning methods for the classification of patients from flow cytometry data - Pierre-André Mikem, Laboratoire de Mathématiques d'Orsay, Metafora biosystems 17:00-19:15 (2h15) › Group-Specific Thresholding to Preserve Fairness of Stochastic Classifiers - Shreya Venugopal, Machine Learning in Information Networks 17:00-19:15 (2h15) › Better Capturing Interactions between Products in Retail: Revisited Negative Sampling for Basket Choice Modeling - Vincent Auriau, Mathématiques et Informatique pour la Complexité et les Systèmes, Artefact Research Center 17:00-19:15 (2h15) › Time Series Motif Discovery: A Comprehensive Evaluation - Valerio Guerrini, CB - Centre Borelli - UMR 9010 17:00-19:15 (2h15) › Set To Be Fair: Set-Valued Classification Under Fairness Constraints - Eyal Cohen, Laboratoire de Probabilités, Statistique et Modélisation, Mathématiques Appliquées Paris 5, Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) 17:00-19:15 (2h15) Dinner 19:30 - 20:30 (1h) Dinner	Breakfast 7:30 - 8:30 (1h) Breakfast Conformal prediction - from a general introduction towards conditional guarantees (2/3) 8:45 - 9:45 (1h) Conformal prediction - from a general introduction towards conditional guarantees (2/3) Conference room Aymeric Dieuleveut Transport based generative modeling and applications to sampling (2/3) 9:45 - 10:45 (1h) Transport based generative modeling and applications to sampling (2/3) Conference room Marylou Gabrié Coffee break 10:45 - 11:15 (30min) Coffee break CT - Session 4 11:15 - 12:15 (1h) CT - Session 4 Conference room › Estimating ratios of normalizing constants using stochastic approximation: the SARIS algorithm - Charlotte Baey, Université de Lille 11:15-11:45 (30min) › Bayesian deep learning, overview and challenges - Julyan Arbel, INRIA 11:45-12:15 (30min) Lunch 12:30 - 13:30 (1h) Lunch Free time 13:30 - 15:30 (2h) Free time CT - Session 5 15:30 - 16:45 (1h15) CT - Session 5 Conference room › Equivariant U-Nets for effective image deblurring: design principles and challenges - Jérémy Scanvic, Laboratoire de Physique, ENS de Lyon, Foxstream 15:30-15:55 (25min) › Conditional Sampling with Score-Based Models via Particle Methods - Stanislas Strasman, Laboratoire de Probabilités, Statistique et Modélisation 15:55-16:20 (25min) › Selecting informative conformal prediction sets - Ulysse Gazin, Laboratoire de Probabilités, Statistique et Modélisation 16:20-16:45 (25min) Coffee break 16:45 - 17:15 (30min) Coffee break Predictive posterior sampling from non-stationnary Gaussian process priors via Diffusion models with application to climate data. 17:15 - 18:05 (50min) Predictive posterior sampling from non-stationnary Gaussian process priors via Diffusion models with application to climate data. Conference room Gabriel Victorino-Cardoso TBA 18:10 - 19:00 (50min) TBA Conference room Aurélien Bellet Dinner 19:30 - 20:30 (1h) Dinner	Breakfast 7:30 - 8:30 (1h) Breakfast Conformal prediction - from a general introduction towards conditional guarantees (3/3) 8:45 - 9:45 (1h) Conformal prediction - from a general introduction towards conditional guarantees (3/3) Conference room Aymeric Dieuleveut Transport based generative modeling and applications to sampling (3/3) 9:45 - 10:45 (1h) Transport based generative modeling and applications to sampling (3/3) Conference room Marylou Gabrié Coffee break 10:45 - 11:15 (30min) Coffee break Awards & closing speech 11:15 - 11:45 (30min) Awards & closing speech Awards & closing speech Lunch 12:30 - 13:30 (1h) Lunch

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