Two-Vertex Generators of Jacobians of Graphs. - David Brandfonbrener; Pat Devlin 001; Netanel Friedenberg; Yuxuan Ke; Steffen Marcus; Henry Reichard; Ethan Sciamma (Electron. J. Comb., 2018)

On the Expected Dynamics of Nonlinear TD Learning. - David Brandfonbrener; Joan Bruna (CoRR, 2019)

Frequentist Regret Bounds for Randomized Least-Squares Value Iteration. - Andrea Zanette; David Brandfonbrener; Matteo Pirotta; Alessandro Lazaric (CoRR, 2019)

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

Geometric Insights into the Convergence of Nonlinear TD Learning. - David Brandfonbrener; Joan Bruna (ICLR, 2020)

Overfitting and Optimization in Offline Policy Learning. - David Brandfonbrener; William F. Whitney; Rajesh Ranganath; Joan Bruna (CoRR, 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)

Offline Contextual Bandits with Overparameterized Models. - David Brandfonbrener; William F. Whitney; Rajesh Ranganath; Joan Bruna (ICML, 2021)

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

Quantile Filtered Imitation Learning. - David Brandfonbrener; William F. Whitney; Rajesh Ranganath; Joan Bruna (CoRR, 2021)

When does return-conditioned supervised learning work for offline reinforcement learning? - David Brandfonbrener; Alberto Bietti; Jacob Buckman; Romain Laroche; Joan Bruna (NeurIPS, 2022)

Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning. - Denis Yarats; David Brandfonbrener; Hao Liu 055; Michael Laskin; Pieter Abbeel; Alessandro Lazaric; Lerrel Pinto (CoRR, 2022)

Incorporating Explicit Uncertainty Estimates into Deep Offline Reinforcement Learning. - David Brandfonbrener; Remi Tachet des Combes; Romain Laroche (CoRR, 2022)

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 (ICRA, 2023)

Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation. - David Brandfonbrener; Ofir Nachum; Joan Bruna (NeurIPS, 2023)

Bridging the Gap from Supervised Learning to Control - David Brandfonbrener (2023)

Q-Probe: A Lightweight Approach to Reward Maximization for Language Models. - Kenneth Li 002; 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 (ICML, 2024)

CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training. - David Brandfonbrener; Hanlin Zhang 002; Andreas Kirsch 002; Jonathan Richard Schwarz; Sham M. Kakade (NeurIPS, 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)

Universal Length Generalization with Turing Programs. - Kaiying Hou; David Brandfonbrener; Sham M. Kakade; Samy Jelassi; Eran Malach (CoRR, 2024)

Deconstructing What Makes a Good Optimizer for Language Models. - Rosie Zhao; Depen Morwani; David Brandfonbrener; Nikhil Vyas 001; Sham M. Kakade (CoRR, 2024)

SOAP: Improving and Stabilizing Shampoo using Adam. - Nikhil Vyas 001; Depen Morwani; Rosie Zhao; Itai Shapira; David Brandfonbrener; Lucas Janson; Sham M. Kakade (CoRR, 2024)

SOAP: Improving and Stabilizing Shampoo using Adam for Language Modeling. - Nikhil Vyas 001; Depen Morwani; Rosie Zhao; Itai Shapira; David Brandfonbrener; Lucas Janson; Sham M. Kakade (ICLR, 2025)

Mixture of Parrots: Experts improve memorization more than reasoning. - Samy Jelassi; Clara Mohri; David Brandfonbrener; Alex Gu; Nikhil Vyas 001; Nikhil Anand; David Alvarez-Melis; Yuanzhi Li; Sham M. Kakade; Eran Malach (ICLR, 2025)

Deconstructing What Makes a Good Optimizer for Autoregressive Language Models. - Rosie Zhao; Depen Morwani; David Brandfonbrener; Nikhil Vyas 001; Sham M. Kakade (ICLR, 2025)

Loss-to-Loss Prediction: Scaling Laws for All Datasets. - David Brandfonbrener; Nikhil Anand; Nikhil Vyas 001; Eran Malach; Sham M. Kakade (Trans. Mach. Learn. Res., 2025)

The Role of Sparsity for Length Generalization in Transformers. - Noah Golowich; Samy Jelassi; David Brandfonbrener; Sham M. Kakade; Eran Malach (CoRR, 2025)