Papers from Criteos at NeurIPS (x 7), TMLR - Criteo AI Lab

Papers from Criteos at NeurIPS (x 7), TMLR

By: Criteo AI Lab / 20 Sep 2022

Papers at NeurIPS 2022

  1. 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](https://arxiv.org/abs/2205.09739)
  2. V. Cabannes, F. Bach, V. Perchet, A. Rudi “Active Labeling: Streaming Stochastic Gradients” Active Labeling: Streaming Stochastic Gradients](https://arxiv.org/abs/2205.13255)
  3. 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: FedPop: A Bayesian Approach for Personalised Federated Learning](https://arxiv.org/abs/2206.03611)
  4. 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”.
  5. 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”. No-Regret Learning in Games with Noisy Feedback: Faster Rates and…](https://arxiv.org/abs/2206.06015)
  6. T.Moreau, M. Massias, A. Gramfort et al (with A. Rakotomamonjy), “Benchopt: Reproducible, efficient and collaborative optimization benchmarks”, Benchopt: Reproducible, efficient and collaborative optimization benchmarks](https://arxiv.org/abs/2206.13424)
  7. 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