| Practical quasi-newton methods for training deep neural networks D Goldfarb, Y Ren, A Bahamou Advances in Neural Information Processing Systems 33, 2386-2396, 2020 | 126 | 2020 |
| Pricing with samples A Allouah, A Bahamou, O Besbes Operations Research 70 (2), 1088-1104, 2022 | 30 | 2022 |
| Optimal pricing with a single point A Allouah, A Bahamou, O Besbes Management Science 69 (10), 5866-5882, 2023 | 24 | 2023 |
| A Mini-Block Fisher Method for Deep Neural Networks A Bahamou, D Goldfarb, Y Ren The 26th International Conference on Artificial Intelligence and Statistics …, 2023 | 15* | 2023 |
| Kronecker-factored Quasi-Newton Methods for Deep Learning Y Ren, A Bahamou, D Goldfarb arXiv preprint arXiv:2102.06737, 2021 | 13* | 2021 |
| Stochastic flows and geometric optimization on the orthogonal group K Choromanski, D Cheikhi, J Davis, V Likhosherstov, A Nazaret, ... International Conference on Machine Learning, 1918-1928, 2020 | 8 | 2020 |
| Revenue maximization from finite samples A Allouah, A Bahamou, O Besbes Proceedings of the 22nd ACM Conference on Economics and Computation, 51-51, 2021 | 5 | 2021 |
| A dynamic sampling adaptive-sgd method for machine learning A Bahamou, D Goldfarb arXiv preprint arXiv:1912.13357, 2019 | 3 | 2019 |
| Fast revenue maximization A Bahamou, O Besbes, O Mouchtaki arXiv preprint arXiv:2407.07316, 2024 | 2 | 2024 |
| Topics in Deep Learning and Data-driven Optimization A Bahamou Columbia University, 2023 | | 2023 |
| Hawkes processes for credit indices time series analysis: How random are trades arrival times? A Bahamou, M Doumergue, P Donnat arXiv preprint arXiv:1902.03714, 2019 | | 2019 |