Research

Hi! PARIS at ICML 2021

19 papers accepted at the international Conference on Machine Learning (ICML)

Hi! PARIS is pleased to present the work of its researchers and professors at the 38th International Conference on Machine Learning (ICML) being held this week (July 18-24, 2021).

For this edition, 19 publications were accepted by the reviewers for their significant contribution in the field of machine learning. The ICML conference is world-renowned for presenting and publishing cutting-edge research on all aspects of machine learning, and is one of the fastest growing AI conferences in the world.

Accepted papers from Hi! PARIS Research Affiliates cover a range of topics of AI. Several schools of Institut Polytechnique de Paris are represented.

Congratulations to our Researchers!

Ecole Polytechnique de Paris, Institut Polytechnique de Paris: Achille Thin, Eric Moulines, Erwan Scornet, George Dasoulas, Konstantinos Chatzikokolakis, Rémi Flamary, Szymon Majewski, Vincent Plassier

ENSAE Paris, Institut Polytechnique de Paris: Marco Cuturi, Anna Korba,  Vianney Perchet, Flore Sentenac, Meyer Scetbon

Telecom Paris, Institut Polytechnique de Paris: Ondřej Cífka, Stephan Clémençon, Ekhine Irurozki, Rémy Leluc, Nathan Noiry, Gaël Richard, Guillaume Staerman, Yannick Guyonvarch

Here is the complete list of ICML publications for Hi! PARIS Research Affiliates:
  1. Relative Positional Encoding for Transformers with Linear Complexity Antoine Liutkus (Inria) · Ondřej Cífka (Télécom Paris, Institut Polytechnique de Paris) · Shih-Lun Wu (National Taiwan University) · Umut Simsekli (Inria/ENS) · Yi-Hsuan Yang (Academia Sinica) · Gaël RICHARD (Télécom Paris, Institut Polytechnique de Paris)
  2. Generalization Bounds in the Presence of Outliers: a Median-of-Means Study Pierre Laforgue (University of Milan) · Guillaume Staerman (Télécom Paris, Institut Polytechnique de Paris) · Stephan Clémençon (Télécom Paris, Institut Polytechnique de Paris)
  3. Feature Clustering for Support Identification in Extreme Regions Hamid Jalalzai (Inria) · Rémi Leluc (Télécom Paris, Institut Polytechnique de Paris)
  4. Learning from Biased Data: A Semi-Parametric Approach Patrice Bertail (Université Paris Nanterre) · Stephan Clémençon (Télécom Paris, Institut Polytechnique de Paris) · Yannick Guyonvarch (Télécom Paris, Institut Polytechnique de Paris) · Nathan NOIRY (Telecom Paris, Institut Polytechnique de Paris)
  5. Concentric mixtures of Mallows models for top-k rankings: sampling and identifiability Fabien Collas (BCAM) · Ekhine IRUROZKI (Telecom Paris, Institut Polytechnique de Paris)
  6. Mixed Nash Equilibria in the Adversarial Examples Game Laurent Meunier (Facebook/Dauphine) · Meyer Scetbon (CREST, ENSAE, Institut Polytechnique de Paris) · Rafael Pinot (Dauphine University – CEA LIST) · Jamal Atif (Université Paris-Dauphine) · Yann Chevaleyre (Univ. Paris Dauphine)
  7. Kernel Stein Discrepancy Descent Anna Korba (CREST, ENSAE Paris, Institut Polytechnique de Paris) · Pierre-Cyril Aubin-Frankowski (MINES ParisTech) · Szymon Majewski (Ecole Polytechnique, Institut Polytechnique de Paris) · Pierre Ablin (CNRS and ENS)
  8. Online A-Optimal Design and Active Linear Regression Xavier Fontaine (ENS Paris-Saclay) · Pierre Perrault (ENS Paris Saclay & Inria) · Michal Valko (DeepMind / Inria / ENS Paris-Saclay) · Vianney Perchet (ENSAE Paris, Institut Polytechnique de Paris & Criteo AI Lab)
  9. Low-Rank Sinkhorn Factorization Meyer Scetbon (CREST, ENSAE Paris, Institut Polytechnique de Paris) · Marco Cuturi (Google) · Gabriel Peyré (CNRS and ENS)
  10. Pure Exploration and Regret Minimization in Matching Bandits Flore Sentenac (CREST, ENSAE, Institut Polytechnique de Paris) · Jialin Yi (London School of Economics) · Clément Calauzènes (Criteo AI Lab) · Vianney Perchet (ENSAE & Criteo AI Lab) · Milan Vojnovic (London School of Economics)
  11. Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction Afsaneh Mastouri (University College London) · Yuchen Zhu (University College London) · Limor Gultchin (University of Oxford) · Anna Korba (CREST, ENSAE, Institut Polytechnique de Paris) · Ricardo Silva (University College London) · Matt J. Kusner (University College London) · Arthur Gretton (Gatsby Computational Neuroscience Unit) · Krikamol Muandet (Max Planck Institute for Intelligent Systems)
  12. Analyzing the tree-layer structure of Deep Forests Ludovic Arnould (Sorbonne Universite) · Claire Boyer (LPSM, Sorbonne Université) · Erwan Scornet (École Polytechnique de Paris, Institut Polytechnique de Paris)
  13. Exact Optimization of Conformal Predictors via Incremental and Decremental Learning  Giovanni Cherubin (Alan Turing Institute) · Konstantinos Chatzikokolakis (Ecole Polytechnique de Paris, Institut Polytechnique de Paris) · Martin Jaggi (EPFL)
  14. DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs Vincent Plassier (Ecole Polytechnique de Paris, Institut Polytechnique de Paris) · Maxime Vono (Lagrange Mathematics and Computing Research Center) · Alain Durmus (ENS Paris Saclay) · Eric Moulines (Ecole Polytechnique de Paris, Institut Polytechnique de Paris)
  15. Monte Carlo Variational Auto-Encoders Achille Thin (Ecole Polytechnique de Paris, Institut Polytechnique de Paris) · Nikita Kotelevskii (Skolkovo Institute of Science and Technology) · Arnaud Doucet (Oxford University) · Alain Durmus (ENS Paris Saclay) · Eric Moulines (Ecole Polytechnique de Paris, Institut Polytechnique de Paris) · Maxim Panov (Skolkovo Institute of Science and Technology)
  16. Lipschitz normalization for self-attention layers with application to graph neural networks George Dasoulas (Ecole Polytechnique de Paris, Institut Polytechnique de Paris) · Kevin Scaman (Noah’s Ark, Huawei Technologies) · Aladin Virmaux (Huawei)
  17. Online Graph Dictionary Learning Cédric Vincent-Cuaz (INRIA Sophia Antipolis) · Titouan Vayer (IRISA) · Rémi Flamary (Ecole Polytechnique de Paris, Institut Polytechnique de Paris) · Marco Corneli (Université Côte d’Azur) · Nicolas Courty (UBS)
  18. Unbalanced minibatch Optimal Transport; applications to Domain Adaptation Kilian Fatras (IRISA/INRIA) · Thibault Séjourné (CNRS, Projet NORIA, ENS, PSL) · Rémi Flamary (Ecole Polytechnique de Paris, Institut Polytechnique de Paris) · Nicolas Courty (UBS)
  19. Counterfactual Credit Assignment in Model-Free Reinforcement Learning Thomas Mesnard (DeepMind) · Theophane Weber (DeepMind) · Fabio Viola (DeepMind) · Shantanu Thakoor (DeepMind) · Alaa Saade (DeepMind) · Anna Harutyunyan (DeepMind) · Will Dabney (DeepMind) · Thomas Stepleton (DeepMind) · Nicolas Heess (DeepMind) · Arthur Guez (Google DeepMind) · Eric Moulines (Ecole Polytechnique de Paris, Institut Polytechnique de Paris) · Marcus Hutter (DeepMind) · Lars Buesing (Deepmind) · Remi Munos (DeepMind)