Tom Zahavy
Tom Zahavy
Senior Research Scientist, DeepMind
Zweryfikowany adres z deepmind.com - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
A deep hierarchical approach to lifelong learning in minecraft
C Tessler, S Givony, T Zahavy, DJ Mankowitz, S Mannor
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence …, 2016
2832016
Graying the black box: Understanding dqns
T Zahavy, N Ben-Zrihem, S Mannor
International Conference on Machine Learning (ICML) 2016, 1899-1908, 2016
1782016
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning
T Zahavy, M Haroush, N Merlis, DJ Mankowitz, S Mannor
Advances in Neural Information Processing Systems (NeurIPS) 2018, 2018
105*2018
Deep learning reconstruction of ultrashort pulses
T Zahavy, A Dikopoltsev, D Moss, GI Haham, O Cohen, S Mannor, ...
Optica 5 (5), 666-673, 2018
742018
Is a picture worth a thousand words? A deep multi-modal architecture for product classification in e-commerce
T Zahavy, A Krishnan, A Magnani, S Mannor
Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018
61*2018
Shallow updates for deep reinforcement learning
N Levine, T Zahavy, DJ Mankowitz, A Tamar, S Mannor
Advances in Neural Information Processing Systems (NeurIPS) 2017, 3135-3145, 2017
382017
Ensemble Robustness and Generalization of Stochastic Deep Learning Algorithms
T Zahavy, B Kang, A Sivak, J Feng, H Xu, S Mannor
International Conference on Learning Representations Workshop (ICLRW'18), 2016
25*2016
A self-tuning actor-critic algorithm
T Zahavy, Z Xu, V Veeriah, M Hessel, J Oh, HP van Hasselt, D Silver, ...
Advances in Neural Information Processing Systems 33, 2020
23*2020
Action assembly: Sparse imitation learning for text based games with combinatorial action spaces
C Tessler, T Zahavy, D Cohen, DJ Mankowitz, S Mannor
RLDM 2019: The Multi-disciplinary Conference on Reinforcement Learning and …, 2019
13*2019
Online Limited Memory Neural-Linear Bandits with Likelihood Matching
O Nabati, T Zahavy, S Mannor
International Conference on Machine Learning (ICML) 2021, 2021
12*2021
Visualizing Dynamics: from t-SNE to SEMI-MDPs
NB Zrihem, T Zahavy, S Mannor
ICML Workshop on Human Interpretability in Machine Learning (WHI 2016),, 2016
11*2016
Deep learning reconstruction of ultrashort pulses from 2D spatial intensity patterns recorded by an all-in-line system in a single-shot
R Ziv, A Dikopoltsev, T Zahavy, I Rubinstein, P Sidorenko, O Cohen, ...
Optics express 28 (5), 7528-7538, 2020
102020
Sub-Nyquist sampling of OFDM signals for cognitive radios
T Zahavy, O Shayer, D Cohen, A Tolmachev, YC Eldar
2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014
92014
Apprenticeship learning via frank-wolfe
T Zahavy, A Cohen, H Kaplan, Y Mansour
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 6720-6728, 2020
82020
Unknown mixing times in apprenticeship and reinforcement learning
T Zahavy, A Cohen, H Kaplan, Y Mansour
Conference on Uncertainty in Artificial Intelligence (UAI), 2020, 2020
8*2020
Train on validation: squeezing the data lemon
G Tennenholtz, T Zahavy, S Mannor
arXiv preprint arXiv:1802.05846, 2018
62018
Emphatic Algorithms for Deep Reinforcement Learning
R Jiang, T Zahavy, Z Xu, A White, M Hessel, C Blundell, H van Hasselt
International Conference on Machine Learning (ICML) 2021, 2021
32021
Planning in hierarchical reinforcement learning: Guarantees for using local policies
T Zahavy, A Hasidim, H Kaplan, Y Mansour
Algorithmic Learning Theory, 906-934, 2020
32020
Inverse reinforcement learning in contextual MDPs
S Belogolovsky, P Korsunsky, S Mannor, C Tessler, T Zahavy
Machine Learning, 1-40, 2021
22021
Online Apprenticeship Learning
L Shani, T Zahavy, S Mannor
arXiv preprint arXiv:2102.06924, 2021
22021
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