David Brandfonbrener

According to our database, David Brandfonbrener authored at least 24 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Loss-to-Loss Prediction: Scaling Laws for All Datasets.

David Brandfonbrener, Nikhil Anand, Nikhil Vyas, Eran Malach, Sham M. Kakade

CoRR, 2024

Mixture of Parrots: Experts improve memorization more than reasoning.

Samy Jelassi, Clara Mohri, David Brandfonbrener, Alex Gu, Nikhil Vyas, Nikhil Anand, David Alvarez-Melis, Yuanzhi Li, Sham M. Kakade, Eran Malach

CoRR, 2024

SOAP: Improving and Stabilizing Shampoo using Adam.

Nikhil Vyas, Depen Morwani, Rosie Zhao, Itai Shapira, David Brandfonbrener, Lucas Janson, Sham M. Kakade

CoRR, 2024

Deconstructing What Makes a Good Optimizer for Language Models.

Rosie Zhao, Depen Morwani, David Brandfonbrener, Nikhil Vyas, Sham M. Kakade

CoRR, 2024

Universal Length Generalization with Turing Programs.

Kaiying Hou, David Brandfonbrener, Sham M. Kakade, Samy Jelassi, Eran Malach

CoRR, 2024

CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training.

David Brandfonbrener, Hanlin Zhang, Andreas Kirsch, Jonathan Richard Schwarz, Sham M. Kakade

CoRR, 2024

Verified Multi-Step Synthesis using Large Language Models and Monte Carlo Tree Search.

David Brandfonbrener, Sibi Raja, Tarun Prasad, Chloe Loughridge, Jianang Yang, Simon Henniger, William E. Byrd, Robert Zinkov, Nada Amin

CoRR, 2024

Repeat After Me: Transformers are Better than State Space Models at Copying.

Samy Jelassi, David Brandfonbrener, Sham M. Kakade, Eran Malach

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Q-Probe: A Lightweight Approach to Reward Maximization for Language Models.

Kenneth Li, Samy Jelassi, Hugh Zhang, Sham M. Kakade, Martin Wattenberg, David Brandfonbrener

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation.

David Brandfonbrener, Ofir Nachum, Joan Bruna

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based Reinforcement Learning.

David Brandfonbrener, Stephen Tu, Avi Singh, Stefan Welker, Chad Boodoo, Nikolai Matni, Jake Varley

Proceedings of the IEEE International Conference on Robotics and Automation, 2023

2022

Incorporating Explicit Uncertainty Estimates into Deep Offline Reinforcement Learning.

David Brandfonbrener, Remi Tachet des Combes, Romain Laroche

CoRR, 2022

Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning.

Denis Yarats, David Brandfonbrener, Hao Liu, Michael Laskin, Pieter Abbeel, Alessandro Lazaric, Lerrel Pinto

CoRR, 2022

When does return-conditioned supervised learning work for offline reinforcement learning?

David Brandfonbrener, Alberto Bietti, Jacob Buckman, Romain Laroche, Joan Bruna

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Quantile Filtered Imitation Learning.

David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna

CoRR, 2021

Offline RL Without Off-Policy Evaluation.

David Brandfonbrener, Will Whitney, Rajesh Ranganath, Joan Bruna

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Offline Contextual Bandits with Overparameterized Models.

David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Evaluating representations by the complexity of learning low-loss predictors.

William F. Whitney, Min Jae Song, David Brandfonbrener, Jaan Altosaar, Kyunghyun Cho

CoRR, 2020

Overfitting and Optimization in Offline Policy Learning.

David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna

CoRR, 2020

Geometric Insights into the Convergence of Nonlinear TD Learning.

David Brandfonbrener, Joan Bruna

Proceedings of the 8th International Conference on Learning Representations, 2020

Frequentist Regret Bounds for Randomized Least-Squares Value Iteration.

Andrea Zanette, David Brandfonbrener, Emma Brunskill, Matteo Pirotta, Alessandro Lazaric

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Frequentist Regret Bounds for Randomized Least-Squares Value Iteration.

Andrea Zanette, David Brandfonbrener, Matteo Pirotta, Alessandro Lazaric

CoRR, 2019

On the Expected Dynamics of Nonlinear TD Learning.

David Brandfonbrener, Joan Bruna

CoRR, 2019

2018

Two-Vertex Generators of Jacobians of Graphs.

David Brandfonbrener, Pat Devlin, Netanel Friedenberg, Yuxuan Ke, Steffen Marcus, Henry Reichard, Ethan Sciamma

Electron. J. Comb., 2018