| Offline rl without off-policy evaluation D Brandfonbrener, W Whitney, R Ranganath, J Bruna Advances in neural information processing systems 34, 4933-4946, 2021 | 184 | 2021 |
| Frequentist regret bounds for randomized least-squares value iteration A Zanette*, D Brandfonbrener*, E Brunskill, M Pirotta, A Lazaric International Conference on Artificial Intelligence and Statistics, 1954-1964, 2020 | 154 | 2020 |
| Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning D Yarats*, D Brandfonbrener*, H Liu, M Laskin, P Abbeel, A Lazaric, ... arXiv preprint arXiv:2201.13425, 2022 | 96 | 2022 |
| When does return-conditioned supervised learning work for offline reinforcement learning? D Brandfonbrener, A Bietti, J Buckman, R Laroche, J Bruna Advances in Neural Information Processing Systems 35, 1542-1553, 2022 | 83 | 2022 |
| Repeat after me: Transformers are better than state space models at copying S Jelassi, D Brandfonbrener, SM Kakade, E Malach arXiv preprint arXiv:2402.01032, 2024 | 54 | 2024 |
| Psychrnn: An accessible and flexible python package for training recurrent neural network models on cognitive tasks DB Ehrlich, JT Stone, D Brandfonbrener, A Atanasov, JD Murray eneuro 8 (1), 2021 | 28 | 2021 |
| Geometric insights into the convergence of nonlinear TD learning D Brandfonbrener, J Bruna International Conference on Learning Representations (ICLR), 2020 | 28* | 2020 |
| Evaluating representations by the complexity of learning low-loss predictors WF Whitney, MJ Song, D Brandfonbrener, J Altosaar, K Cho arXiv preprint arXiv:2009.07368, 2020 | 27 | 2020 |
| Inverse dynamics pretraining learns good representations for multitask imitation D Brandfonbrener, O Nachum, J Bruna Advances in Neural Information Processing Systems 36, 2023 | 16 | 2023 |
| Offline Contextual Bandits with Overparameterized Models D Brandfonbrener, WF Whitney, R Ranganath, J Bruna International Conference on Machine Learning (ICML), 2021, 2020 | 16* | 2020 |
| Soap: Improving and stabilizing shampoo using adam N Vyas, D Morwani, R Zhao, I Shapira, D Brandfonbrener, L Janson, ... arXiv preprint arXiv:2409.11321, 2024 | 13 | 2024 |
| VerMCTS: Synthesizing Multi-Step Programs using a Verifier, a Large Language Model, and Tree Search D Brandfonbrener, S Henniger, S Raja, T Prasad, C Loughridge, ... arXiv preprint arXiv:2402.08147, 2024 | 13* | 2024 |
| Deconstructing what makes a good optimizer for language models R Zhao*, D Morwani*, D Brandfonbrener*, N Vyas*, S Kakade arXiv preprint arXiv:2407.07972, 2024 | 10 | 2024 |
| Visual backtracking teleoperation: A data collection protocol for offline image-based reinforcement learning D Brandfonbrener, S Tu, A Singh, S Welker, C Boodoo, N Matni, J Varley 2023 IEEE International Conference on Robotics and Automation (ICRA), 11336 …, 2023 | 10 | 2023 |
| Quantile filtered imitation learning D Brandfonbrener, WF Whitney, R Ranganath, J Bruna arXiv preprint arXiv:2112.00950, 2021 | 8 | 2021 |
| Incorporating explicit uncertainty estimates into deep offline reinforcement learning D Brandfonbrener, RT Combes, R Laroche arXiv preprint arXiv:2206.01085, 2022 | 6 | 2022 |
| Universal length generalization with turing programs K Hou, D Brandfonbrener, S Kakade, S Jelassi, E Malach arXiv preprint arXiv:2407.03310, 2024 | 5 | 2024 |
| CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training D Brandfonbrener, H Zhang, A Kirsch, JR Schwarz, S Kakade arXiv preprint arXiv:2406.10670, 2024 | 4 | 2024 |
| Mixture of parrots: Experts improve memorization more than reasoning S Jelassi, C Mohri, D Brandfonbrener, A Gu, N Vyas, N Anand, ... arXiv preprint arXiv:2410.19034, 2024 | 3 | 2024 |
| Q-Probe: A Lightweight Approach to Reward Maximization for Language Models K Li, S Jelassi, H Zhang, S Kakade, M Wattenberg, D Brandfonbrener arXiv preprint arXiv:2402.14688, 2024 | 3 | 2024 |
Joan BrunaProfessor of Computer Science, Data Science & Mathematics (aff), Courant Institute and CDS, NYUcims.nyu.edu의 이메일 확인됨