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ORCIDTed Moskovitz
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- affiliation: University College London (UCL), Gatsby Computational Neuroscience Unit, UK
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2020 – today
- 2025
[c12]Aaditya K. Singh, Ted Moskovitz, Sara Dragutinovic, Felix Hill, Stephanie C. Y. Chan, Andrew M. Saxe:
Strategy Coopetition Explains the Emergence and Transience of In-Context Learning. ICML 2025- [i16]Aaditya K. Singh, Ted Moskovitz, Sara Dragutinovic, Felix Hill, Stephanie C. Y. Chan, Andrew M. Saxe:
Strategy Coopetition Explains the Emergence and Transience of In-Context Learning. CoRR abs/2503.05631 (2025) - 2024
- [j1]Ted Moskovitz
, Kevin J. Miller, Maneesh Sahani, Matthew M. Botvinick:
Understanding dual process cognition via the minimum description length principle. PLoS Comput. Biol. 20(10): 1012383 (2024) - [c11]Ted Moskovitz, Aaditya K. Singh, DJ Strouse, Tuomas Sandholm, Ruslan Salakhutdinov, Anca D. Dragan, Stephen Marcus McAleer:
Confronting Reward Model Overoptimization with Constrained RLHF. ICLR 2024 - [c10]Aaditya K. Singh, Ted Moskovitz, Felix Hill, Stephanie C. Y. Chan, Andrew M. Saxe:
What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation. ICML 2024: 45637-45662 - [i15]Aaditya K. Singh, Ted Moskovitz, Felix Hill, Stephanie C. Y. Chan, Andrew M. Saxe:
What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation. CoRR abs/2404.07129 (2024) - [i14]Albert S. Yue, Lovish Madaan, Ted Moskovitz, DJ Strouse, Aaditya K. Singh:
HARP: A challenging human-annotated math reasoning benchmark. CoRR abs/2412.08819 (2024) - 2023
- [c9]Ted Moskovitz, Ta-Chu Kao, Maneesh Sahani, Matt M. Botvinick:
Minimum Description Length Control. ICLR 2023 - [c8]Ted Moskovitz, Brendan O'Donoghue, Vivek Veeriah, Sebastian Flennerhag, Satinder Singh, Tom Zahavy:
ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs. ICML 2023: 25303-25336 - [c7]Ted Moskovitz, Samo Hromadka, Ahmed Touati, Diana Borsa, Maneesh Sahani:
A State Representation for Diminishing Rewards. NeurIPS 2023 - [c6]Aaditya K. Singh, Stephanie C. Y. Chan, Ted Moskovitz, Erin Grant, Andrew M. Saxe, Felix Hill:
The Transient Nature of Emergent In-Context Learning in Transformers. NeurIPS 2023 - [i13]Ted Moskovitz, Brendan O'Donoghue, Vivek Veeriah, Sebastian Flennerhag, Satinder Singh, Tom Zahavy:
ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs. CoRR abs/2302.01275 (2023) - [i12]Ted Moskovitz, Samo Hromadka, Ahmed Touati, Diana Borsa, Maneesh Sahani:
A State Representation for Diminishing Rewards. CoRR abs/2309.03710 (2023) - [i11]Ted Moskovitz, Aaditya K. Singh, DJ Strouse, Tuomas Sandholm, Ruslan Salakhutdinov, Anca D. Dragan, Stephen McAleer:
Confronting Reward Model Overoptimization with Constrained RLHF. CoRR abs/2310.04373 (2023) - [i10]Aaditya K. Singh, Stephanie C. Y. Chan, Ted Moskovitz, Erin Grant, Andrew M. Saxe, Felix Hill:
The Transient Nature of Emergent In-Context Learning in Transformers. CoRR abs/2311.08360 (2023) - 2022
- [c5]Ted Moskovitz, Michael Arbel, Jack Parker-Holder, Aldo Pacchiano:
Towards an Understanding of Default Policies in Multitask Policy Optimization. AISTATS 2022: 10661-10686 - [c4]Ted Moskovitz, Spencer R. Wilson, Maneesh Sahani:
A First-Occupancy Representation for Reinforcement Learning. ICLR 2022 - [i9]Ted Moskovitz, Ta-Chu Kao, Maneesh Sahani, Matthew M. Botvinick:
Minimum Description Length Control. CoRR abs/2207.08258 (2022) - [i8]Abhi Gupta, Ted Moskovitz, David Alvarez-Melis, Aldo Pacchiano:
Transfer RL via the Undo Maps Formalism. CoRR abs/2211.14469 (2022) - 2021
- [c3]Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton:
Efficient Wasserstein Natural Gradients for Reinforcement Learning. ICLR 2021 - [c2]Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel, Michael I. Jordan:
Tactical Optimism and Pessimism for Deep Reinforcement Learning. NeurIPS 2021: 12849-12863 - [i7]Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel:
Deep Reinforcement Learning with Dynamic Optimism. CoRR abs/2102.03765 (2021) - [i6]Ted Moskovitz, Spencer R. Wilson, Maneesh Sahani:
A First-Occupancy Representation for Reinforcement Learning. CoRR abs/2109.13863 (2021) - [i5]Ted Moskovitz, Michael Arbel, Jack Parker-Holder, Aldo Pacchiano:
Towards an Understanding of Default Policies in Multitask Policy Optimization. CoRR abs/2111.02994 (2021) - 2020
- [c1]Li K. Wenliang, Theodore H. Moskovitz, Heishiro Kanagawa, Maneesh Sahani:
Amortised Learning by Wake-Sleep. ICML 2020: 10236-10247 - [i4]Li Kevin Wenliang, Theodore H. Moskovitz, Heishiro Kanagawa, Maneesh Sahani:
Amortised Learning by Wake-Sleep. CoRR abs/2002.09737 (2020) - [i3]Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton:
Efficient Wasserstein Natural Gradients for Reinforcement Learning. CoRR abs/2010.05380 (2020)
2010 – 2019
- 2019
- [i2]Ted Moskovitz, Rui Wang, Janice Lan, Sanyam Kapoor, Thomas Miconi, Jason Yosinski, Aditya Rawal:
First-Order Preconditioning via Hypergradient Descent. CoRR abs/1910.08461 (2019) - 2018
- [i1]Theodore H. Moskovitz, Ashok Litwin-Kumar, L. F. Abbott:
Feedback alignment in deep convolutional networks. CoRR abs/1812.06488 (2018)
Coauthor Index
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