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 (such as linear complexity transformers, learning from bias data, Reinforcement learning, Causal learning, Online Graph dictionary learning, domain adaption with optimal transport, etc.). Several schools of Institut Polytechnique de Paris are represented.

Congratulations to our Researchers!

École polytechnique, IP PARIS: Achille Thin, Eric Moulines, Erwan Scornet, George Dasoulas, Konstantinos Chatzikokolakis, Rémi Flamary, Szymon Majewski, Vincent Plassier

ENSAE Paris, IP PARIS: Marco Cuturi, Anna Korba,  Vianney Perchet, Flore Sentenac, Meyer Scetbon

Telecom Paris, IP 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 – IP PARIS) · Shih-Lun Wu (National Taiwan University) · Umut Simsekli (Inria/ENS) · Yi-Hsuan Yang (Academia Sinica) · Gaël Richard (Télécom Paris – IP PARIS)
    Article available on
  2. Generalization Bounds in the Presence of Outliers: a Median-of-Means Study
    Pierre Laforgue (University of Milan) · Guillaume Staerman (Télécom Paris – IP PARIS) · Stephan Clémençon (Télécom Paris – IP PARIS)
    Article available on
  3. Feature Clustering for Support Identification in Extreme RegionsHamid Jalalzai (Inria) · Rémi Leluc (Télécom Paris – IP PARIS)
    Article available on
  4. Learning from Biased Data: A Semi-Parametric Approach
    Patrice Bertail (Université Paris Nanterre) · Stephan Clémençon (Télécom Paris – IP PARIS) · Yannick Guyonvarch (Télécom Paris – IP PARIS) · Nathan NOIRY (Telecom Paris – IP PARIS)
    Article available on
  5. Concentric mixtures of Mallows models for top-k rankings: sampling and identifiability
    Fabien Collas (BCAM) · Ekhine IRUROZKI (Telecom Paris – IP PARIS)
    Article available on
  6. Mixed Nash Equilibria in the Adversarial Examples Game
    Laurent Meunier (Facebook/Dauphine) · Meyer Scetbon (CREST, ENSAE – IP PARIS) · Rafael Pinot (Dauphine University – CEA LIST) · Jamal Atif (Université Paris-Dauphine) · Yann Chevaleyre (Univ. Paris Dauphine)
    Article available on
  7. Kernel Stein Discrepancy Descent
    Anna Korba (CREST, ENSAE Paris – IP PARIS) · Pierre-Cyril Aubin-Frankowski (MINES ParisTech) · Szymon Majewski (Ecole Polytechnique – IP PARIS) · Pierre Ablin (CNRS and ENS)
    Article available on
  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 – IP PARIS & Criteo AI Lab)
    Article available on
  9. Low-Rank Sinkhorn Factorization
    Meyer Scetbon (CREST, ENSAE Paris – IP PARIS) · Marco Cuturi (Google) · Gabriel Peyré (CNRS and ENS
    Article available on
  10. Pure Exploration and Regret Minimization in Matching Bandits
    Flore Sentenac (CREST, ENSAE – IP PARIS) · Jialin Yi (London School of Economics) · Clément Calauzènes (Criteo AI Lab) · Vianney Perchet (ENSAE & Criteo AI Lab) · Milan Vojnovic (London School of Economics)
    Article available on
  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 – IP 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)
    Article available on
  12. Analyzing the tree-layer structure of Deep Forests
    Ludovic Arnould (Sorbonne Universite) · Claire Boyer (LPSM, Sorbonne Université) · Erwan Scornet (École Polytechnique de Paris – IP PARIS)
    Article available on
  13. Exact Optimization of Conformal Predictors via Incremental and Decremental Learning
    Giovanni Cherubin (Alan Turing Institute) · Konstantinos Chatzikokolakis (École polytechnique – IP PARIS) · Martin Jaggi (EPFL)
    Article available on
  14. DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs
    Vincent Plassier (École polytechnique – IP PARIS) · Maxime Vono (Lagrange Mathematics and Computing Research Center) · Alain Durmus (ENS Paris Saclay) · Eric Moulines (École polytechnique – IP PARIS)
    Article available on
  15. Monte Carlo Variational Auto-Encoders
    Achille Thin (École polytechnique – IP PARIS) · Nikita Kotelevskii (Skolkovo Institute of Science and Technology) · Arnaud Doucet (Oxford University) · Alain Durmus (ENS Paris Saclay) · Eric Moulines (École polytechnique – IP PARIS) · Maxim Panov (Skolkovo Institute of Science and Technology)
    Article available on
  16. Lipschitz normalization for self-attention layers with application to graph neural networks
    George Dasoulas (École polytechnique – IP PARIS) · Kevin Scaman (Noah’s Ark, Huawei Technologies) · Aladin Virmaux (Huawei)
    Article available on
  17. Online Graph Dictionary Learning
    Cédric Vincent-Cuaz (INRIA Sophia Antipolis) · Titouan Vayer (IRISA) · Rémi Flamary (École polytechnique – IP PARIS) · Marco Corneli (Université Côte d’Azur) · Nicolas Courty (UBS)
    Article available on
  18. Unbalanced minibatch Optimal Transport; applications to Domain Adaptation
    Kilian Fatras (IRISA/INRIA) · Thibault Séjourné (CNRS, Projet NORIA, ENS, PSL) · Rémi Flamary (École polytechnique – IP PARIS) · Nicolas Courty (UBS)
    Article available on
  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 (École polytechnique – IP PARIS) · Marcus Hutter (DeepMind) · Lars Buesing (Deepmind) · Remi Munos (DeepMind)
    Article available on