Papers at NeurIPS 2022
- Alexandre Ramé (Sorbonne Université), Matthieu Kirchmeyer (Sorbonne Université & Criteo AI Lab), Thibaud Rahier (Criteo AI Lab), Alain Rakotomamonjy (Université de Rouen LITIS & Criteo AI Lab), Patrick Gallinari (Sorbonne Université & Criteo AI Lab), Matthieu Cord (Sorbonne Université & Valeo.ai) “Diverse weight averaging for out-of-distribution generalization”
Diverse Weight Averaging for Out-of-Distribution Generalization
- V. Cabannes, F. Bach, V. Perchet, A. Rudi “Active Labeling: Streaming Stochastic Gradients”
- N. Kotelevskii (Skoltech), M. Vono (Criteo AI Lab), E. Moulines (Polytechnique), A. Durmus (ENS Paris Saclay), “FedPop: A Bayesian Approach for Personalised Federated Learning”, link:
- Angeliki Giannou, Kyriakos Lotidis, Panagiotis Mertikopoulos, and Emmanouil Vasileios Vlatakis-Gkaragkounis, “On the convergence of policy gradient methods to Nash equilibria in general stochastic games”.
- Yu-Guan Hsieh, Kimon Antonakopoulos, Volkan Cevher, and Panagiotis Mertikopoulos, “No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation”.
- T.Moreau, M. Massias, A. Gramfort et al (with A. Rakotomamonjy), “Benchopt: Reproducible, efficient and collaborative optimization benchmarks”,
- Neurips 2022 – Systems Datasets and Benchmarks Track: Florent Bonnet (Extrality and Sorbonne University), Jocelyn Ahmed Mazari (Extrality), Paola Cinnella (Sorbonne University), Patrick Gallinari (Sorbonne Université & Criteo AI Lab), AirfoilRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged-Navier–Stokes Solutions https://openreview.net/pdf?id=Zp8YmiQ_bDC
Paper at TMLR
- (Accepted Sep. 2022): Zhiqiang Zhong (University of Luxembourg), Sergey Ivanov (Criteo AI Lab), Jun Pang (University of Luxembourg) “Simplifying Node Classification on Heterophilous Graphs with Compatible Label Propagation“.
Simplifying Node Classification on Heterophilous Graphs with…