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  • Edouard Oyallon

  • Chargé de Recherche
  • Équipe: MLIA

Publications

  • Louis Fournier, Edouard Oyallon. Cyclic Data Parallelism for Efficient Parallelism of Deep Neural Networks. 2024. ⟨hal-04500345⟩
  • Léo Grinsztajn, Myung Jun Kim, Edouard Oyallon, Gaël Varoquaux. Vectorizing string entries for data processing on tables: when are larger language models better?. 2023. ⟨hal-04345931⟩
  • Adel Nabli, Eugene Belilovsky, Edouard Oyallon. $\textbf{A}^2\textbf{CiD}^2$: Accelerating Asynchronous Communication in Decentralized Deep Learning. Thirty-seventh Conference on Neural Information Processing Systems, Dec 2023, New Orleans, United States. ⟨hal-04124318v2⟩
  • Gwen Legate, Nicolas Bernier, Lucas Caccia, Edouard Oyallon, Eugene Belilovsky. Guiding The Last Layer in Federated Learning with Pre-Trained Models. Neurips, In press. ⟨hal-04262471⟩
  • Edouard Oyallon. Contributions to Local, Asynchronous and Decentralized Learning, and to Geometric Deep Learning. Artificial Intelligence [cs.AI]. Sorbonne Université, 2023. ⟨tel-04334118⟩
  • Louis Fournier, Adeetya Patel, Michael Eickenberg, Edouard Oyallon, Eugene Belilovsky. Preventing Dimensional Collapse in Contrastive Local Learning with Subsampling. ICML 2023 Workshop on Localized Learning (LLW), Jul 2023, Honolulu (Hawaii), USA, United States. ⟨hal-04156218⟩
  • Adel Nabli, Edouard Oyallon. DADAO: Decoupled Accelerated Decentralized Asynchronous Optimization. International Conference on Machine Learning, Jul 2023, Honolulu, United States. ⟨hal-03737694v3⟩
  • Louis Fournier, Stéphane Rivaud, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon. Can Forward Gradient Match Backpropagation?. Fortieth International Conference on Machine Learning, Jul 2023, Honolulu (Hawaii), USA, United States. ⟨hal-04119829⟩
  • Grégoire Sergeant-Perthuis, Jakob Maier, Joan Bruna, Edouard Oyallon. On Non-Linear operators for Geometric Deep Learning. Conference on Neural Information Processing Systems (Neurips), Dec 2022, New Orleans, United States. ⟨10.48550/arXiv.2207.03485⟩. ⟨hal-03711864v2⟩
  • Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux. Why do tree-based models still outperform deep learning on typical tabular data?. 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks, Nov 2022, New Orleans, United States. ⟨hal-03723551v3⟩
  • Irene Tenison, Sai Aravind Sreeramadas, Vaikkunth Mugunthan, Edouard Oyallon, Eugene Belilovsky, et al.. Gradient Masked Averaging for Federated Learning. Transactions on Machine Learning Research Journal, 2022. ⟨hal-04262447⟩