# Papers from Criteos at NeurIPS (x 7), TMLR

By: [Criteo AI Lab](https://ailab.criteo.com/author/mlforum/) / 20 Sep 2022

Papers at [NeurIPS 2022](https://nips.cc/)

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](https://openreview.net/pdf?id=Zp8YmiQ_bDC "https://openreview.net/pdf?id=Zp8YmiQ_bDC")

Paper at [TMLR](https://www.jmlr.org/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…](https://openreview.net/forum?id=JBuCfkmKYu)