Ted Moskovitz

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graduate student studying machine learning and neuroscience

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About Me

I’m a fourth year PhD student studying machine learning and theoretical neuroscience at the Gatsby Computational Neuroscience Unit, where I’m fortunate to be advised by Maneesh Sahani and Matt Botvinick. Broadly speaking, I’m primarily interested in the complex interrelationship between biological and artificial intelligence and in leveraging the algorithms used by the brain to improve machine learning, particularly reinforcement learning (RL). Right now, I’m focused on multitask and meta-RL with a particular focus on enabling agents to identify and exploit shared structure among tasks. I’m also excited by optimization theory and its applications to RL. Last summer, I was lucky enough to do an internship with Tom Zahavy as part of the Discovery team at DeepMind, where I worked on optimization for constrained RL.

Before arriving at Gatsby, I worked with the Horizons and Deep Collective teams at Uber AI Labs on optimization techniques for deep learning. Previously, I was a Masters student in computer science at Columbia, where I worked with Larry Abbott, Ashok Litwin-Kumar, and Ken Miller at the Center for Theoretical Neuroscience on biologically plausible learning rules and architectures for deep learning. I did my undergrad at Princeton, where I majored in neuroscience with minors in computer science and linguistics, and was advised by Jonathan Pillow.

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Email: ted@gatsby.ucl.ac.uk

Publications and Preprints

Moskovitz T, O’Donoghue B, Veeriah V, Flennerhag S, Singh S, Zahavy T (2023). ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs. Preprint. paper | website

Moskovitz T, Kao T, Sahani M*, Botvinick M* (2023). Minimum Description Length Control. International Conference on Learning Representations (ICLR). *Equal Contribution. To appear. paper

Moskovitz T, Miller K, Sahani M, Botvinick M (2022). A Unified Theory of Dual Process Control. Preprint. paper

Moskovitz T, Arbel M, Parker-Holder J, Pacchiano A (2022). Towards an Understanding of Default Policies in Multitask Policy Optimization. Conference on Artificial Intelligence and Statistics (AISTATS). paper (Best Paper Award Honorable Mention)

Moskovitz T, Wilson SR, Sahani M (2022). A First-Occupancy Representation for Reinforcement Learning. International Conference on Learning Representations (ICLR). paper | code | talk at Cosyne

Moskovitz T, Parker-Holder J, Pacchiano A, Arbel M, Jordan MI (2021). Tactical Optimism and Pessimism for Deep Reinforcement Learning. Neural Information Processing Systems (NeurIPS). paper | code

Moskovitz T*, Arbel M*, Huszar F, Gretton A (2021). Efficient Wasserstein Natural Gradients for Reinforcement Learning. International Conference on Learning Representations (ICLR). *Equal contribution. paper | code

Li WK, Moskovitz T, Kanagawa H, Sahani M (2020). Amortised Learning by Wake-Sleep. International Conference on Machine Learning (ICML). paper | code

Moskovitz T, Wang R, Lan J, Kapoor S, Yosinski J, Rawal A (2019). Learned First-Order Preconditioning. Beyond First Order Methods in ML Workshop, Neural Information Processing Systems. paper

Lindsay G, Moskovitz T, Yang G, Miller K (2019). Do Biologically-Plausible Architectures Produce Biologically-Realistic Models? Conference on Cognitive Computational Neuroscience. paper

Sun M, Li J, Moskovitz T, Lindsay G, Miller K, Dipoppa M, Yang G (2019). Understanding the Functional and Structural Differences Across Excitatory and Inhibitory Neurons. Conference on Cognitive Computational Neuroscience. paper

Moskovitz T, Litwin-Kumar A, Abbott LF (2018). Feedback alignment in deep convolutional networks. Pre-print. paper

Moskovitz T, Roy NA, Pillow JW (2018). A comparison of deep learning and linear-nonlinear cascade models to neural encoding. Preprint. paper

Hsu E, Fowler E, Staudt L, Greenberg M, Moskovitz T, Shattuck DW, Joshi SH (2016). DTI of corticospinal tracts pre- and post-physical therapy in children with cerebral palsy. Proceedings of the Organization of Human Brain Mapping. poster

Notes

Gatsby Theoretical Neuroscience Course Guide