Ted Moskovitz

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Aldo PacchianoBroad Institute of MIT and HarvardVerified email at broadinstitute.org
Michael ArbelInria - Univ. Grenoble AlpesVerified email at inria.fr
Maneesh SahaniGatsby Unit, UCLVerified email at gatsby.ucl.ac.uk
Jack Parker-HolderGoogle DeepMindVerified email at google.com
L F AbbottBrandeis University, CERN, Boston University, Columbia University, Stanford Linear AcceleratorVerified email at columbia.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Ferenc HuszárUniversity of CambridgeVerified email at cam.ac.uk
Arthur GrettonGatsby Computational Neuroscience Unit and DeepmindVerified email at gatsby.ucl.ac.uk
Jonathan PillowProfessor of Neuroscience, Princeton UniversityVerified email at princeton.edu
Kenneth D. MillerProfessor of Neuroscience, Columbia UniversityVerified email at columbia.edu
Grace W LindsayAssistant Professor, New York UniversityVerified email at nyu.edu
Matthew BotvinickGoogle DeepMind, Yale Law School, University College LondonVerified email at google.com
Sebastian FlennerhagResearch Scientist at DeepMindVerified email at google.com
Satinder SinghGoogle DeepMind / U. of MichiganVerified email at umich.edu
Tom ZahavySenior Research Scientist, Google DeepMindVerified email at deepmind.com
Vivek VeeriahDeepMindVerified email at deepmind.com
Brendan O'DonoghueStanford University, DeepMindVerified email at alumni.stanford.edu

Ted Moskovitz

Gatsby Unit, UCL

Verified email at gatsby.ucl.ac.uk - Homepage

Reinforcement Learning Deep Learning Neuroscience Optimization


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Feedback alignment in deep convolutional networks TH Moskovitz, A Litwin-Kumar, LF Abbott arXiv preprint arXiv:1812.06488, 2018	56	2018
Tactical Optimism and Pessimism for Deep Reinforcement Learning T Moskovitz, J Parker-Holder, A Pacchiano, M Arbel, MI Jordan Neural Information Processing Systems (NeurIPS) 2021, 2021	28*	2021
Efficient Wasserstein Natural Gradients for Reinforcement Learning T Moskovitz, M Arbel, F Huszar, A Gretton International Conference on Learning Representations (ICLR) 2021, 2020	15	2020
A First-Occupancy Representation for Reinforcement Learning T Moskovitz, SR Wilson, M Sahani International Conference on Learning Representations (ICLR) 2022, 2021	10	2021
A comparison of deep learning and linear-nonlinear cascade approaches to neural encoding TH Moskovitz, NA Roy, JW Pillow BioRxiv, 463422, 2018	10	2018
Towards an Understanding of Default Policies in Multitask Policy Optimization T Moskovitz, M Arbel, J Parker-Holder, A Pacchiano Conference on Artificial Intelligence and Statistics (AISTATS) 2022, 2021	8	2021
First-Order Preconditioning via Hypergradient Descent T Moskovitz, R Wang, J Lan, S Kapoor, T Miconi, J Yosinski, A Rawal NeurIPS 2019, Beyond First Order Methods in ML Workshop, 2019	7*	2019
ReLOAD: Reinforcement learning with optimistic ascent-descent for last-iterate convergence in constrained mdps T Moskovitz, B O’Donoghue, V Veeriah, S Flennerhag, S Singh, T Zahavy International Conference on Machine Learning, 25303-25336, 2023	4	2023
Action prediction error: a value-free dopaminergic teaching signal that drives stable learning F Greenstreet, HM Vergara, S Pati, L Schwarz, M Wisdom, F Marbach, ... bioRxiv, 2022	4	2022
Amortised Learning by Wake-Sleep LK Wenliang, T Moskovitz, H Kanagawa, M Sahani International Conference on Machine Learning (ICML) 2020, 2020	4	2020
A unified theory of dual-process control T Moskovitz, K Miller, M Sahani, MM Botvinick arXiv preprint arXiv:2211.07036, 2022	3	2022
Understanding the functional and structural differences across excitatory and inhibitory neurons S Minni, L Ji-An, T Moskovitz, G Lindsay, K Miller, M Dipoppa, GR Yang bioRxiv, 680439, 2019	2	2019
Do Biologically-Realistic Recurrent Architectures Produce Biologically-Realistic Models? G Lindsay, T Moskovitz, GR Yang, K Miller Conference on Cognitive Computational Neuroscience (CCN) 2019, 2019	1	2019
A State Representation for Diminishing Rewards T Moskovitz, S Hromadka, A Touati, D Borsa, M Sahani arXiv preprint arXiv:2309.03710, 2023		2023
Undo Maps: A Tool for Adapting Policies to Perceptual Distortions A Gupta, T Moskovitz, D Alvarez-Melis, A Pacchiano ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems, 2023		2023
Minimum Description Length Control T Moskovitz, TC Kao, M Sahani, MM Botvinick International Conference on Learning Representations (ICLR) 2023, 2022		2022
Deep Transfer Learning for Language Generation from Limited Corpora T Moskovitz		2018

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