Ted Moskovitz

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Citations	109	109
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Michael ArbelInria - Univ. Grenoble AlpesVerified email at inria.fr
Aldo PacchianoMicrosoft ResearchVerified email at microsoft.com
Jack Parker-HolderResearch Scientist at DeepMindVerified email at google.com
Maneesh SahaniGatsby Unit, UCLVerified email at gatsby.ucl.ac.uk
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
Jonathan PillowProfessor of Neuroscience, Princeton UniversityVerified email at princeton.edu
Ferenc HuszárUniversity of CambridgeVerified email at cam.ac.uk
Arthur GrettonGatsby Computational Neuroscience Unit, UCLVerified email at gatsby.ucl.ac.uk
Kenneth D. MillerProfessor of Neuroscience, Columbia UniversityVerified email at columbia.edu
Grace W LindsayPostdoc, University College LondonVerified email at ucl.ac.uk
Matthew BotvinickDeepMind, London, UKVerified email at google.com

Ted Moskovitz

Gatsby Unit, UCL

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

Reinforcement Learning Theoretical 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	48	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	17*	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
Efficient Wasserstein Natural Gradients for Reinforcement Learning T Moskovitz, M Arbel, F Huszar, A Gretton International Conference on Learning Representations (ICLR) 2021, 2020	9	2020
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	6	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	6*	2019
A First-Occupancy Representation for Reinforcement Learning T Moskovitz, SR Wilson, M Sahani International Conference on Learning Representations (ICLR) 2022, 2021	5	2021
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	3	2022
Amortised Learning by Wake-Sleep LK Wenliang, T Moskovitz, H Kanagawa, M Sahani International Conference on Machine Learning (ICML) 2020, 2020	3	2020
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
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	1	2019
Transfer RL via the Undo Maps Formalism A Gupta, T Moskovitz, D Alvarez-Melis, A Pacchiano arXiv preprint arXiv:2211.14469, 2022		2022
A Unified Theory of Dual-Process Control T Moskovitz, K Miller, M Sahani, MM Botvinick arXiv preprint arXiv:2211.07036, 2022		2022
Minimum Description Length Control T Moskovitz, TC Kao, M Sahani, MM Botvinick International Conference on Learning Representations (ICLR) 2023, 2022		2022

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