Tom Zahavy

Publication Activity (10 Years)

Years Active: 2014-2023
Publications (10 Years): 70

Top Topics

Learning Algorithm

Reinforcement Learning

Theoretical Justification

Top Venues

Publications

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. GECCO Companion (2023)
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)
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)
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. GECCO (2023)
Sebastian Flennerhag, Tom Zahavy, Brendan O'Donoghue, Hado Philip van Hasselt, András György, Satinder Singh
Optimistic Meta-Gradients. NeurIPS (2023)
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 (2023)
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)
Veronica Chelu, Tom Zahavy, Arthur Guez, Doina Precup, Sebastian Flennerhag
Optimism and Adaptivity in Policy Optimization. CoRR (2023)
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)
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 (2023)
Sebastian Flennerhag, Tom Zahavy, Brendan O'Donoghue, Hado van Hasselt, András György, Satinder Singh
Optimistic Meta-Gradients. CoRR (2023)
Hadar Schreiber Galler, Tom Zahavy, Guillaume Desjardins, Alon Cohen
APART: Diverse Skill Discovery using All Pairs with Ascending Reward and DropouT. CoRR (2023)
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 (2023)
Hao Liu, Tom Zahavy, Volodymyr Mnih, Satinder Singh
Palm up: Playing in the Latent Manifold for Unsupervised Pretraining. NeurIPS (2022)
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 (2022)
Hao Liu, Tom Zahavy, Volodymyr Mnih, Satinder Singh
Palm up: Playing in the Latent Manifold for Unsupervised Pretraining. CoRR (2022)
Khimya Khetarpal, Claire Vernade, Brendan O'Donoghue, Satinder Singh, Tom Zahavy
POMRL: No-Regret Learning-to-Plan with Increasing Horizons. CoRR (2022)
Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy, Hado van Hasselt, David Silver, Satinder Singh
Bootstrapped Meta-Learning. ICLR (2022)
Lior Shani, Tom Zahavy, Shie Mannor
Online Apprenticeship Learning. AAAI (2022)
Tom Zahavy, Yannick Schroecker, Feryal Behbahani, Kate Baumli, Sebastian Flennerhag, Shaobo Hou, Satinder Singh
Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality. CoRR (2022)
Jelena Luketina, Sebastian Flennerhag, Yannick Schroecker, David Abel, Tom Zahavy, Satinder Singh
Meta-Gradients in Non-Stationary Environments. CoRR (2022)
Jelena Luketina, Sebastian Flennerhag, Yannick Schroecker, David Abel, Tom Zahavy, Satinder Singh
Meta-Gradients in Non-Stationary Environments. CoLLAs (2022)
Tom Zahavy, Brendan O'Donoghue, André Barreto, Volodymyr Mnih, Sebastian Flennerhag, Satinder Singh
Discovering Diverse Nearly Optimal Policies withSuccessor Features. CoRR (2021)
Lior Shani, Tom Zahavy, Shie Mannor
Online Apprenticeship Learning. CoRR (2021)
Ray Jiang, Tom Zahavy, Zhongwen Xu, Adam White, Matteo Hessel, Charles Blundell, Hado van Hasselt
Emphatic Algorithms for Deep Reinforcement Learning. ICML (2021)
Ofir Nabati, Tom Zahavy, Shie Mannor
Online Limited Memory Neural-Linear Bandits with Likelihood Matching. CoRR (2021)
Tom Zahavy, Brendan O'Donoghue, Guillaume Desjardins, Satinder Singh
Reward is enough for convex MDPs. NeurIPS (2021)
Ray Jiang, Tom Zahavy, Zhongwen Xu, Adam White, Matteo Hessel, Charles Blundell, Hado van Hasselt
Emphatic Algorithms for Deep Reinforcement Learning. CoRR (2021)
Tom Zahavy, André Barreto, Daniel J. Mankowitz, Shaobo Hou, Brendan O'Donoghue, Iurii Kemaev, Satinder Baveja Singh
Discovering a set of policies for the worst case reward. CoRR (2021)
Tom Zahavy, Brendan O'Donoghue, Guillaume Desjardins, Satinder Singh
Reward is enough for convex MDPs. CoRR (2021)
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 (2021)
Stav Belogolovsky, Philip Korsunsky, Shie Mannor, Chen Tessler, Tom Zahavy
Inverse reinforcement learning in contextual MDPs. Mach. Learn. 110 (9) (2021)
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)
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)
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)
Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy, Hado van Hasselt, David Silver, Satinder Singh
Bootstrapped Meta-Learning. CoRR (2021)
Ofir Nabati, Tom Zahavy, Shie Mannor
Online Limited Memory Neural-Linear Bandits with Likelihood Matching. ICML (2021)
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)
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 (2020)
Tom Zahavy, Alon Cohen, Haim Kaplan, Yishay Mansour
Unknown mixing times in apprenticeship and reinforcement learning. UAI (2020)
Tom Zahavy, Avinatan Hassidim, Haim Kaplan, Yishay Mansour
Planning in Hierarchical Reinforcement Learning: Guarantees for Using Local Policies. ALT (2020)
Uri Shaham, Tom Zahavy, Cesar Caraballo, Shiwani Mahajan, Daisy Massey, Harlan M. Krumholz
Learning to Ask Medical Questions using Reinforcement Learning. CoRR (2020)
Tom Zahavy, Alon Cohen, Haim Kaplan, Yishay Mansour
Apprenticeship Learning via Frank-Wolfe. AAAI (2020)
Uri Shaham, Tom Zahavy, Cesar Caraballo, Shiwani Mahajan, Daisy Massey, Harlan M. Krumholz
Learning to Ask Medical Questions using Reinforcement Learning. MLHC (2020)
Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
Self-Tuning Deep Reinforcement Learning. CoRR (2020)
Philip Korsunsky, Stav Belogolovsky, Tom Zahavy, Chen Tessler, Shie Mannor
Inverse Reinforcement Learning in Contextual MDPs. CoRR (2019)
Tom Zahavy, Avinatan Hassidim, Haim Kaplan, Yishay Mansour
Planning in Hierarchical Reinforcement Learning: Guarantees for Using Local Policies. CoRR (2019)
Tom Zahavy, Alon Cohen, Haim Kaplan, Yishay Mansour
Apprenticeship Learning via Frank-Wolfe. CoRR (2019)
Tom Zahavy, Alon Cohen, Haim Kaplan, Yishay Mansour
Average reward reinforcement learning with unknown mixing times. CoRR (2019)
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 (2019)
Tom Zahavy, Shie Mannor
Deep Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matching. CoRR (2019)
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 (2019)
Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J. Mankowitz, Shie Mannor
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning. NeurIPS (2018)
Matan Haroush, Tom Zahavy, Daniel J. Mankowitz, Shie Mannor
Learning How Not to Act in Text-based Games. ICLR (Workshop) (2018)
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)
Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J. Mankowitz, Shie Mannor
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning. CoRR (2018)
Guy Tennenholtz, Tom Zahavy, Shie Mannor
Train on Validation: Squeezing the Data Lemon. CoRR (2018)
Tom Zahavy, Alex Dikopoltsev, Oren Cohen, Shie Mannor, Mordechai Segev
Deep Learning Reconstruction of Ultra-Short Pulses. CoRR (2018)
Tom Zahavy, Avinatan Hassidim, Haim Kaplan, Yishay Mansour
Hierarchical Reinforcement Learning: Approximating Optimal Discounted TSP Using Local Policies. CoRR (2018)
Tom Zahavy, Bingyi Kang, Alex Sivak, Jiashi Feng, Huan Xu, Shie Mannor
Ensemble Robustness and Generalization of Stochastic Deep Learning Algorithms. ICLR (Workshop) (2018)
Nir Levine, Tom Zahavy, Daniel J. Mankowitz, Aviv Tamar, Shie Mannor
Shallow Updates for Deep Reinforcement Learning. CoRR (2017)
Nir Levine, Tom Zahavy, Daniel J. Mankowitz, Aviv Tamar, Shie Mannor
Shallow Updates for Deep Reinforcement Learning. NIPS (2017)
Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J. Mankowitz, Shie Mannor
A Deep Hierarchical Approach to Lifelong Learning in Minecraft. AAAI (2017)
Jiashi Feng, Tom Zahavy, Bingyi Kang, Huan Xu, Shie Mannor
Ensemble Robustness of Deep Learning Algorithms. CoRR (2016)
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 (2016)
Nir Baram, Tom Zahavy, Shie Mannor
Deep Reinforcement Learning Discovers Internal Models. CoRR (2016)
Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J. Mankowitz, Shie Mannor
A Deep Hierarchical Approach to Lifelong Learning in Minecraft. CoRR (2016)
Nir Ben-Zrihem, Tom Zahavy, Shie Mannor
Visualizing Dynamics: from t-SNE to SEMI-MDPs. CoRR (2016)
Tom Zahavy, Nir Ben-Zrihem, Shie Mannor
Graying the black box: Understanding DQNs. CoRR (2016)
Tom Zahavy, Nir Ben-Zrihem, Shie Mannor
Graying the black box: Understanding DQNs. ICML (2016)
Tom Zahavy, Oran Shayer, Deborah Cohen, Alex Tolmachev, Yonina C. Eldar
Sub-Nyquist sampling of OFDM signals for cognitive radios. ICASSP (2014)