Feedback alignment in deep convolutional networks. - Theodore H. Moskovitz; Ashok Litwin-Kumar; L. F. Abbott (CoRR, 2018)

First-Order Preconditioning via Hypergradient Descent. - Ted Moskovitz; Rui Wang 052; Janice Lan; Sanyam Kapoor; Thomas Miconi; Jason Yosinski; Aditya Rawal (CoRR, 2019)

Amortised Learning by Wake-Sleep. - Li K. Wenliang; Theodore H. Moskovitz; Heishiro Kanagawa; Maneesh Sahani (ICML, 2020)

Efficient Wasserstein Natural Gradients for Reinforcement Learning. - Ted Moskovitz; Michael Arbel; Ferenc Huszar; Arthur Gretton (ICLR, 2021)

Tactical Optimism and Pessimism for Deep Reinforcement Learning. - Ted Moskovitz; Jack Parker-Holder; Aldo Pacchiano; Michael Arbel; Michael I. Jordan (NeurIPS, 2021)

Deep Reinforcement Learning with Dynamic Optimism. - Ted Moskovitz; Jack Parker-Holder; Aldo Pacchiano; Michael Arbel (CoRR, 2021)

Towards an Understanding of Default Policies in Multitask Policy Optimization. - Ted Moskovitz; Michael Arbel; Jack Parker-Holder; Aldo Pacchiano (AISTATS, 2022)

A First-Occupancy Representation for Reinforcement Learning. - Ted Moskovitz; Spencer R. Wilson; Maneesh Sahani (ICLR, 2022)

Transfer RL via the Undo Maps Formalism. - Abhi Gupta; Ted Moskovitz; David Alvarez-Melis; Aldo Pacchiano (CoRR, 2022)

Minimum Description Length Control. - Ted Moskovitz; Ta-Chu Kao; Maneesh Sahani; Matt M. Botvinick (ICLR, 2023)

ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs. - Ted Moskovitz; Brendan O'Donoghue; Vivek Veeriah; Sebastian Flennerhag; Satinder Singh 001; Tom Zahavy (ICML, 2023)

A State Representation for Diminishing Rewards. - Ted Moskovitz; Samo Hromadka; Ahmed Touati; Diana Borsa; Maneesh Sahani (NeurIPS, 2023)

The Transient Nature of Emergent In-Context Learning in Transformers. - Aaditya K. Singh; Stephanie C. Y. Chan; Ted Moskovitz; Erin Grant; Andrew M. Saxe; Felix Hill (NeurIPS, 2023)

Confronting Reward Model Overoptimization with Constrained RLHF. - Ted Moskovitz; Aaditya K. Singh; DJ Strouse; Tuomas Sandholm; Ruslan Salakhutdinov; Anca D. Dragan; Stephen Marcus McAleer (ICLR, 2024)

What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation. - Aaditya K. Singh; Ted Moskovitz; Felix Hill; Stephanie C. Y. Chan; Andrew M. Saxe (ICML, 2024)

HARP: A challenging human-annotated math reasoning benchmark. - Albert S. Yue; Lovish Madaan; Ted Moskovitz; DJ Strouse; Aaditya K. Singh (CoRR, 2024)

Understanding dual process cognition via the minimum description length principle. - Ted Moskovitz; Kevin J. Miller; Maneesh Sahani; Matthew M. Botvinick (PLoS Comput. Biol., 2024)

Strategy Coopetition Explains the Emergence and Transience of In-Context Learning. - Aaditya K. Singh; Ted Moskovitz; Sara Dragutinovic; Felix Hill; Stephanie C. Y. Chan; Andrew M. Saxe (CoRR, 2025)