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Tom Zahavy
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2020 – today
- 2023
- [j2]Khimya Khetarpal, Claire Vernade, Brendan O'Donoghue, Satinder Singh, Tom Zahavy:
POMRL: No-Regret Learning-to-Plan with Increasing Horizons. Trans. Mach. Learn. Res. 2023 (2023) - [c29]Robert Tjarko Lange, Tom Schaul
, Yutian Chen
Discovering Evolution Strategies via Meta-Black-Box Optimization. GECCO Companion 2023: 29-30 - [c28]Robert Tjarko Lange, Tom Schaul
Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization. GECCO 2023: 929-937 - [c27]Robert Tjarko Lange, Tom Schaul, Yutian Chen, Tom Zahavy, Valentin Dalibard, Chris Lu, Satinder Singh, Sebastian Flennerhag:
Discovering Evolution Strategies via Meta-Black-Box Optimization. ICLR 2023 - [c26]Tom Zahavy, Yannick Schroecker, Feryal M. P. Behbahani, Kate Baumli, Sebastian Flennerhag, Shaobo Hou, Satinder Singh:
Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality. ICLR 2023 - [c25]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 - [c24]Sebastian Flennerhag, Tom Zahavy, Brendan O'Donoghue, Hado Philip van Hasselt, András György, Satinder Singh:
Optimistic Meta-Gradients. NeurIPS 2023 - [i40]Sebastian Flennerhag, Tom Zahavy, Brendan O'Donoghue, Hado van Hasselt, András György, Satinder Singh:
Optimistic Meta-Gradients. CoRR abs/2301.03236 (2023) - [i39]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) - [i38]Robert Tjarko Lange, Tom Schaul, Yutian Chen, Chris Lu, Tom Zahavy, Valentin Dalibard, Sebastian Flennerhag:
Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization. CoRR abs/2304.03995 (2023) - [i37]Veronica Chelu, Tom Zahavy, Arthur Guez, Doina Precup, Sebastian Flennerhag:
Optimism and Adaptivity in Policy Optimization. CoRR abs/2306.10587 (2023) - [i36]Tom Zahavy, Vivek Veeriah, Shaobo Hou, Kevin Waugh, Matthew Lai, Edouard Leurent, Nenad Tomasev, Lisa Schut, Demis Hassabis, Satinder Singh:
Diversifying AI: Towards Creative Chess with AlphaZero. CoRR abs/2308.09175 (2023) - [i35]Hadar Schreiber Galler, Tom Zahavy, Guillaume Desjardins, Alon Cohen:
APART: Diverse Skill Discovery using All Pairs with Ascending Reward and DropouT. CoRR abs/2308.12649 (2023) - 2022
- [c23]Lior Shani, Tom Zahavy, Shie Mannor:
Online Apprenticeship Learning. AAAI 2022: 8240-8248 - [c22]Jelena Luketina, Sebastian Flennerhag, Yannick Schroecker, David Abel, Tom Zahavy, Satinder Singh:
Meta-Gradients in Non-Stationary Environments. CoLLAs 2022: 886-901 - [c21]Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy, Hado van Hasselt, David Silver, Satinder Singh:
Bootstrapped Meta-Learning. ICLR 2022 - [c20]Hao Liu, Tom Zahavy, Volodymyr Mnih, Satinder Singh:
Palm up: Playing in the Latent Manifold for Unsupervised Pretraining. NeurIPS 2022 - [i34]Tom Zahavy, Yannick Schroecker, Feryal M. P. Behbahani, Kate Baumli, Sebastian Flennerhag, Shaobo Hou, Satinder Singh:
Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality. CoRR abs/2205.13521 (2022) - [i33]Jelena Luketina, Sebastian Flennerhag, Yannick Schroecker, David Abel, Tom Zahavy, Satinder Singh:
Meta-Gradients in Non-Stationary Environments. CoRR abs/2209.06159 (2022) - [i32]Hao Liu, Tom Zahavy, Volodymyr Mnih, Satinder Singh:
Palm up: Playing in the Latent Manifold for Unsupervised Pretraining. CoRR abs/2210.10913 (2022) - [i31]Robert Tjarko Lange, Tom Schaul, Yutian Chen, Tom Zahavy, Valentin Dallibard, Chris Lu, Satinder Singh, Sebastian Flennerhag:
Discovering Evolution Strategies via Meta-Black-Box Optimization. CoRR abs/2211.11260 (2022) - [i30]Khimya Khetarpal, Claire Vernade, Brendan O'Donoghue, Satinder Singh, Tom Zahavy:
POMRL: No-Regret Learning-to-Plan with Increasing Horizons. CoRR abs/2212.14530 (2022) - 2021
- [j1]Stav Belogolovsky
Inverse reinforcement learning in contextual MDPs. Mach. Learn. 110(9): 2295-2334 (2021) - [c19]Dan A. Calian, Daniel J. Mankowitz, Tom Zahavy, Zhongwen Xu, Junhyuk Oh, Nir Levine, Timothy A. Mann:
Balancing Constraints and Rewards with Meta-Gradient D4PG. ICLR 2021 - [c18]Tom Zahavy, André Barreto, Daniel J. Mankowitz, Shaobo Hou, Brendan O'Donoghue, Iurii Kemaev, Satinder Singh:
Discovering a set of policies for the worst case reward. ICLR 2021 - [c17]Ray Jiang, Tom Zahavy, Zhongwen Xu, Adam White, Matteo Hessel, Charles Blundell, Hado van Hasselt:
Emphatic Algorithms for Deep Reinforcement Learning. ICML 2021: 5023-5033 - [c16]Ofir Nabati, Tom Zahavy, Shie Mannor:
Online Limited Memory Neural-Linear Bandits with Likelihood Matching. ICML 2021: 7905-7915 - [c15]Tom Zahavy, Brendan O'Donoghue, Guillaume Desjardins, Satinder Singh:
Reward is enough for convex MDPs. NeurIPS 2021: 25746-25759 - [c14]Vivek Veeriah, Tom Zahavy, Matteo Hessel, Zhongwen Xu, Junhyuk Oh, Iurii Kemaev, Hado van Hasselt, David Silver, Satinder Singh:
Discovery of Options via Meta-Learned Subgoals. NeurIPS 2021: 29861-29873 - [i29]Ofir Nabati, Tom Zahavy, Shie Mannor:
Online Limited Memory Neural-Linear Bandits with Likelihood Matching. CoRR abs/2102.03799 (2021) - [i28]Tom Zahavy, André Barreto, Daniel J. Mankowitz, Shaobo Hou, Brendan O'Donoghue, Iurii Kemaev, Satinder Singh:
Discovering a set of policies for the worst case reward. CoRR abs/2102.04323 (2021) - [i27]Vivek Veeriah, Tom Zahavy, Matteo Hessel, Zhongwen Xu, Junhyuk Oh, Iurii Kemaev, Hado van Hasselt, David Silver, Satinder Singh:
Discovery of Options via Meta-Learned Subgoals. CoRR abs/2102.06741 (2021) - [i26]Lior Shani, Tom Zahavy, Shie Mannor:
Online Apprenticeship Learning. CoRR abs/2102.06924 (2021) - [i25]Tom Zahavy, Brendan O'Donoghue, Guillaume Desjardins, Satinder Singh:
Reward is enough for convex MDPs. CoRR abs/2106.00661 (2021) - [i24]Tom Zahavy, Brendan O'Donoghue, André Barreto, Volodymyr Mnih, Sebastian Flennerhag, Satinder Singh:
Discovering Diverse Nearly Optimal Policies withSuccessor Features. CoRR abs/2106.00669 (2021) - [i23]Ray Jiang, Tom Zahavy, Zhongwen Xu, Adam White, Matteo Hessel, Charles Blundell, Hado van Hasselt:
Emphatic Algorithms for Deep Reinforcement Learning. CoRR abs/2106.11779 (2021) - [i22]Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy, Hado van Hasselt, David Silver, Satinder Singh:
Bootstrapped Meta-Learning. CoRR abs/2109.04504 (2021) - 2020
- [c13]Tom Zahavy, Alon Cohen, Haim Kaplan, Yishay Mansour:
Apprenticeship Learning via Frank-Wolfe. AAAI 2020: 6720-6728 - [c12]Tom Zahavy, Avinatan Hassidim, Haim Kaplan, Yishay Mansour:
Planning in Hierarchical Reinforcement Learning: Guarantees for Using Local Policies. ALT 2020: 906-934 - [c11]Uri Shaham, Tom Zahavy, Cesar Caraballo, Shiwani Mahajan, Daisy Massey, Harlan M. Krumholz:
Learning to Ask Medical Questions using Reinforcement Learning. MLHC 2020: 2-26 - [c10]Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh:
A Self-Tuning Actor-Critic Algorithm. NeurIPS 2020 - [c9]Tom Zahavy, Alon Cohen, Haim Kaplan, Yishay Mansour:
Unknown mixing times in apprenticeship and reinforcement learning. UAI 2020: 430-439 - [i21]Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh:
Self-Tuning Deep Reinforcement Learning. CoRR abs/2002.12928 (2020) - [i20]Uri Shaham, Tom Zahavy, Cesar Caraballo, Shiwani Mahajan, Daisy Massey, Harlan M. Krumholz:
Learning to Ask Medical Questions using Reinforcement Learning. CoRR abs/2004.00994 (2020) - [i19]Dan A. Calian, Daniel J. Mankowitz, Tom Zahavy, Zhongwen Xu, Junhyuk Oh, Nir Levine, Timothy A. Mann:
Balancing Constraints and Rewards with Meta-Gradient D4PG. CoRR abs/2010.06324 (2020)
2010 – 2019
- 2019
- [i18]Tom Zahavy, Shie Mannor:
Deep Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matching. CoRR abs/1901.08612 (2019) - [i17]Tom Zahavy, Avinatan Hassidim, Haim Kaplan, Yishay Mansour:
Planning in Hierarchical Reinforcement Learning: Guarantees for Using Local Policies. CoRR abs/1902.10140 (2019) - [i16]Chen Tessler, Tom Zahavy, Deborah Cohen, Daniel J. Mankowitz, Shie Mannor:
Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces. CoRR abs/1905.09700 (2019) - [i15]Tom Zahavy, Alon Cohen, Haim Kaplan, Yishay Mansour:
Average reward reinforcement learning with unknown mixing times. CoRR abs/1905.09704 (2019) - [i14]Philip Korsunsky, Stav Belogolovsky, Tom Zahavy, Chen Tessler, Shie Mannor:
Inverse Reinforcement Learning in Contextual MDPs. CoRR abs/1905.09710 (2019) - [i13]Tom Zahavy, Alon Cohen, Haim Kaplan, Yishay Mansour:
Apprenticeship Learning via Frank-Wolfe. CoRR abs/1911.01679 (2019) - [i12]Ron Ziv, Alex Dikopoltsev, Tom Zahavy, Ittai Rubinstein, Pavel Sidorenko, Oren Cohen, Mordechai Segev:
Deep learning reconstruction of ultrashort pulses from 2D spatial intensity patterns recorded by an all-in-line system in a single-shot. CoRR abs/1911.10326 (2019) - 2018
- [c8]Tom Zahavy, Abhinandan Krishnan, Alessandro Magnani, Shie Mannor:
Is a Picture Worth a Thousand Words? A Deep Multi-Modal Architecture for Product Classification in E-Commerce. AAAI 2018: 7873-7881 - [c7]Matan Haroush, Tom Zahavy, Daniel J. Mankowitz, Shie Mannor:
Learning How Not to Act in Text-based Games. ICLR (Workshop) 2018 - [c6]Tom Zahavy, Bingyi Kang, Alex Sivak, Jiashi Feng, Huan Xu, Shie Mannor:
Ensemble Robustness and Generalization of Stochastic Deep Learning Algorithms. ICLR (Workshop) 2018 - [c5]Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J. Mankowitz, Shie Mannor:
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning. NeurIPS 2018: 3566-3577 - [i11]Guy Tennenholtz, Tom Zahavy, Shie Mannor:
Train on Validation: Squeezing the Data Lemon. CoRR abs/1802.05846 (2018) - [i10]Tom Zahavy, Avinatan Hassidim, Haim Kaplan, Yishay Mansour:
Hierarchical Reinforcement Learning: Approximating Optimal Discounted TSP Using Local Policies. CoRR abs/1803.04674 (2018) - [i9]Tom Zahavy, Alex Dikopoltsev, Oren Cohen, Shie Mannor, Mordechai Segev:
Deep Learning Reconstruction of Ultra-Short Pulses. CoRR abs/1803.06024 (2018) - [i8]Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J. Mankowitz, Shie Mannor:
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning. CoRR abs/1809.02121 (2018) - 2017
- [c4]Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J. Mankowitz, Shie Mannor:
A Deep Hierarchical Approach to Lifelong Learning in Minecraft. AAAI 2017: 1553-1561 - [c3]Nir Levine, Tom Zahavy, Daniel J. Mankowitz, Aviv Tamar, Shie Mannor:
Shallow Updates for Deep Reinforcement Learning. NIPS 2017: 3135-3145 - [i7]Nir Levine, Tom Zahavy, Daniel J. Mankowitz, Aviv Tamar, Shie Mannor:
Shallow Updates for Deep Reinforcement Learning. CoRR abs/1705.07461 (2017) - 2016
- [c2]Tom Zahavy, Nir Ben-Zrihem, Shie Mannor:
Graying the black box: Understanding DQNs. ICML 2016: 1899-1908 - [i6]Jiashi Feng, Tom Zahavy, Bingyi Kang, Huan Xu, Shie Mannor:
Ensemble Robustness of Deep Learning Algorithms. CoRR abs/1602.02389 (2016) - [i5]Tom Zahavy, Nir Ben-Zrihem, Shie Mannor:
Graying the black box: Understanding DQNs. CoRR abs/1602.02658 (2016) - [i4]Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J. Mankowitz, Shie Mannor:
A Deep Hierarchical Approach to Lifelong Learning in Minecraft. CoRR abs/1604.07255 (2016) - [i3]Nir Baram, Tom Zahavy, Shie Mannor:
Deep Reinforcement Learning Discovers Internal Models. CoRR abs/1606.05174 (2016) - [i2]Nir Ben-Zrihem, Tom Zahavy, Shie Mannor:
Visualizing Dynamics: from t-SNE to SEMI-MDPs. CoRR abs/1606.07112 (2016) - [i1]Tom Zahavy, Alessandro Magnani, Abhinandan Krishnan, Shie Mannor:
Is a picture worth a thousand words? A Deep Multi-Modal Fusion Architecture for Product Classification in e-commerce. CoRR abs/1611.09534 (2016) - 2014
- [c1]Tom Zahavy, Oran Shayer, Deborah Cohen, Alex Tolmachev, Yonina C. Eldar:
Sub-Nyquist sampling of OFDM signals for cognitive radios. ICASSP 2014: 8092-8096
Coauthor Index
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