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David Brandfonbrener
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2020 – today
- 2024
- [c9]Kenneth Li, Samy Jelassi, Hugh Zhang, Sham M. Kakade, Martin Wattenberg, David Brandfonbrener:
Q-Probe: A Lightweight Approach to Reward Maximization for Language Models. ICML 2024 - [c8]Samy Jelassi, David Brandfonbrener, Sham M. Kakade, Eran Malach:
Repeat After Me: Transformers are Better than State Space Models at Copying. ICML 2024 - [i18]Samy Jelassi, David Brandfonbrener, Sham M. Kakade, Eran Malach:
Repeat After Me: Transformers are Better than State Space Models at Copying. CoRR abs/2402.01032 (2024) - [i17]David Brandfonbrener, Sibi Raja, Tarun Prasad, Chloe Loughridge, Jianang Yang, Simon Henniger, William E. Byrd, Robert Zinkov, Nada Amin
:
Verified Multi-Step Synthesis using Large Language Models and Monte Carlo Tree Search. CoRR abs/2402.08147 (2024) - [i16]Kenneth Li, Samy Jelassi, Hugh Zhang, Sham M. Kakade, Martin Wattenberg, David Brandfonbrener:
Q-Probe: A Lightweight Approach to Reward Maximization for Language Models. CoRR abs/2402.14688 (2024) - [i15]David Brandfonbrener, Hanlin Zhang, Andreas Kirsch, Jonathan Richard Schwarz, Sham M. Kakade:
CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training. CoRR abs/2406.10670 (2024) - [i14]Kaiying Hou, David Brandfonbrener, Sham M. Kakade, Samy Jelassi, Eran Malach:
Universal Length Generalization with Turing Programs. CoRR abs/2407.03310 (2024) - [i13]Rosie Zhao, Depen Morwani, David Brandfonbrener, Nikhil Vyas, Sham M. Kakade:
Deconstructing What Makes a Good Optimizer for Language Models. CoRR abs/2407.07972 (2024) - [i12]Nikhil Vyas, Depen Morwani, Rosie Zhao, Itai Shapira, David Brandfonbrener, Lucas Janson, Sham M. Kakade:
SOAP: Improving and Stabilizing Shampoo using Adam. CoRR abs/2409.11321 (2024) - 2023
- [c7]David Brandfonbrener, Stephen Tu, Avi Singh, Stefan Welker, Chad Boodoo, Nikolai Matni, Jake Varley:
Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based Reinforcement Learning. ICRA 2023: 11336-11342 - [c6]David Brandfonbrener, Ofir Nachum, Joan Bruna:
Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation. NeurIPS 2023 - [i11]David Brandfonbrener, Ofir Nachum, Joan Bruna:
Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation. CoRR abs/2305.16985 (2023) - 2022
- [c5]David Brandfonbrener, Alberto Bietti, Jacob Buckman, Romain Laroche, Joan Bruna:
When does return-conditioned supervised learning work for offline reinforcement learning? NeurIPS 2022 - [i10]Denis Yarats, David Brandfonbrener, Hao Liu, Michael Laskin, Pieter Abbeel, Alessandro Lazaric, Lerrel Pinto:
Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning. CoRR abs/2201.13425 (2022) - [i9]David Brandfonbrener, Alberto Bietti, Jacob Buckman, Romain Laroche, Joan Bruna:
When does return-conditioned supervised learning work for offline reinforcement learning? CoRR abs/2206.01079 (2022) - [i8]David Brandfonbrener, Remi Tachet des Combes, Romain Laroche:
Incorporating Explicit Uncertainty Estimates into Deep Offline Reinforcement Learning. CoRR abs/2206.01085 (2022) - [i7]David Brandfonbrener, Stephen Tu, Avi Singh, Stefan Welker, Chad Boodoo, Nikolai Matni, Jake Varley:
Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based Reinforcement Learning. CoRR abs/2210.02343 (2022) - 2021
- [c4]David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna:
Offline Contextual Bandits with Overparameterized Models. ICML 2021: 1049-1058 - [c3]David Brandfonbrener, Will Whitney, Rajesh Ranganath, Joan Bruna:
Offline RL Without Off-Policy Evaluation. NeurIPS 2021: 4933-4946 - [i6]David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna:
Offline RL Without Off-Policy Evaluation. CoRR abs/2106.08909 (2021) - [i5]David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna:
Quantile Filtered Imitation Learning. CoRR abs/2112.00950 (2021) - 2020
- [c2]Andrea Zanette, David Brandfonbrener, Emma Brunskill, Matteo Pirotta, Alessandro Lazaric:
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration. AISTATS 2020: 1954-1964 - [c1]David Brandfonbrener, Joan Bruna:
Geometric Insights into the Convergence of Nonlinear TD Learning. ICLR 2020 - [i4]David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna:
Overfitting and Optimization in Offline Policy Learning. CoRR abs/2006.15368 (2020) - [i3]William F. Whitney, Min Jae Song, David Brandfonbrener, Jaan Altosaar, Kyunghyun Cho:
Evaluating representations by the complexity of learning low-loss predictors. CoRR abs/2009.07368 (2020)
2010 – 2019
- 2019
- [i2]David Brandfonbrener, Joan Bruna:
On the Expected Dynamics of Nonlinear TD Learning. CoRR abs/1905.12185 (2019) - [i1]Andrea Zanette, David Brandfonbrener, Matteo Pirotta, Alessandro Lazaric:
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration. CoRR abs/1911.00567 (2019) - 2018
- [j1]David Brandfonbrener, Pat Devlin, Netanel Friedenberg, Yuxuan Ke, Steffen Marcus, Henry Reichard, Ethan Sciamma:
Two-Vertex Generators of Jacobians of Graphs. Electron. J. Comb. 25(1): 1 (2018)
Coauthor Index
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