Brandfonbrener, David

22 publications

ICLR 2025 Deconstructing What Makes a Good Optimizer for Autoregressive Language Models Rosie Zhao, Depen Morwani, David Brandfonbrener, Nikhil Vyas, Sham M. Kakade

TMLR 2025 Loss-to-Loss Prediction: Scaling Laws for All Datasets David Brandfonbrener, Nikhil Anand, Nikhil Vyas, Eran Malach, Sham M. Kakade

ICLR 2025 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

ICLR 2025 SOAP: Improving and Stabilizing Shampoo Using Adam for Language Modeling Nikhil Vyas, Depen Morwani, Rosie Zhao, Itai Shapira, David Brandfonbrener, Lucas Janson, Sham M. Kakade

ICML 2025 The Role of Sparsity for Length Generalization in LLMs Noah Golowich, Samy Jelassi, David Brandfonbrener, Sham M. Kakade, Eran Malach

ICML 2025 Universal Length Generalization with Turing Programs Kaiying Hou, David Brandfonbrener, Sham M. Kakade, Samy Jelassi, Eran Malach

NeurIPS 2024 CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-Training David Brandfonbrener, Hanlin Zhang, Andreas Kirsch, Jonathan Richard Schwarz, Sham Kakade

NeurIPSW 2024 Deconstructing What Makes a Good Optimizer for Language Models Rosie Zhao, Depen Morwani, David Brandfonbrener, Nikhil Vyas, Sham M. Kakade

NeurIPSW 2024 Mixture of Parrots: Mixtures of 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

ICML 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

ICML 2024 Repeat After Me: Transformers Are Better than State Space Models at Copying Samy Jelassi, David Brandfonbrener, Sham M. Kakade, Eran Malach

NeurIPSW 2024 SOAP: Improving and Stabilizing Shampoo Using Adam Nikhil Vyas, Depen Morwani, Rosie Zhao, Itai Shapira, David Brandfonbrener, Lucas Janson, Sham M. Kakade

NeurIPSW 2024 VerMCTS: Synthesizing Multi-Step Programs Using a Verifier, a Large Language Model, and Tree Search David Brandfonbrener, Simon Henniger, Sibi Raja, Tarun Prasad, Chloe R Loughridge, Federico Cassano, Sabrina Ruixin Hu, Jianang Yang, William E. Byrd, Robert Zinkov, Nada Amin

NeurIPS 2023 Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation David Brandfonbrener, Ofir Nachum, Joan Bruna

ICLRW 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

NeurIPSW 2022 Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based RL David Brandfonbrener, Stephen Tu, Avi Singh, Stefan Welker, Chad Boodoo, Nikolai Matni, Jake Varley

NeurIPS 2022 When Does Return-Conditioned Supervised Learning Work for Offline Reinforcement Learning? David Brandfonbrener, Alberto Bietti, Jacob Buckman, Romain Laroche, Joan Bruna

ICLRW 2021 Evaluating Representations by the Complexity of Learning Low-Loss Predictors William F Whitney, Min Jae Song, David Brandfonbrener, Jaan Altosaar, Kyunghyun Cho

ICML 2021 Offline Contextual Bandits with Overparameterized Models David Brandfonbrener, William Whitney, Rajesh Ranganath, Joan Bruna

NeurIPS 2021 Offline RL Without Off-Policy Evaluation David Brandfonbrener, Will Whitney, Rajesh Ranganath, Joan Bruna

AISTATS 2020 Frequentist Regret Bounds for Randomized Least-Squares Value Iteration Andrea Zanette, David Brandfonbrener, Emma Brunskill, Matteo Pirotta, Alessandro Lazaric

ICLR 2020 Geometric Insights into the Convergence of Nonlinear TD Learning David Brandfonbrener, Joan Bruna