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Guy Tennenholtz
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2020 – today
- 2024
- [c16]Craig Boutilier, Martin Mladenov, Guy Tennenholtz:
Recommender Ecosystems: A Mechanism Design Perspective on Holistic Modeling and Optimization. AAAI 2024: 22575-22583 - [c15]Alizée Pace, Hugo Yèche, Bernhard Schölkopf, Gunnar Rätsch, Guy Tennenholtz:
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding. ICLR 2024 - [c14]Guy Tennenholtz, Yinlam Chow, Chih-Wei Hsu, Jihwan Jeong, Lior Shani, Azamat Tulepbergenov, Deepak Ramachandran, Martin Mladenov, Craig Boutilier:
Demystifying Embedding Spaces using Large Language Models. ICLR 2024 - [c13]Marek Petrik, Guy Tennenholtz, Mohammad Ghavamzadeh:
Bayesian Regret Minimization in Offline Bandits. ICML 2024 - [i26]Anthony Liang, Guy Tennenholtz, Chih-Wei Hsu, Yinlam Chow, Erdem Biyik, Craig Boutilier:
DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning. CoRR abs/2402.15957 (2024) - [i25]Guy Tennenholtz, Yinlam Chow, Chih-Wei Hsu, Lior Shani, Ethan Liang, Craig Boutilier:
Embedding-Aligned Language Models. CoRR abs/2406.00024 (2024) - [i24]Ori Linial, Guy Tennenholtz, Uri Shalit:
Benchmarks for Reinforcement Learning with Biased Offline Data and Imperfect Simulators. CoRR abs/2407.00806 (2024) - 2023
- [c12]Pranav Khanna, Guy Tennenholtz, Nadav Merlis, Shie Mannor, Chen Tessler:
Never Worse, Mostly Better: Stable Policy Improvement in Deep Reinforcement Learning. AAMAS 2023: 2430-2432 - [c11]Ofir Nabati, Guy Tennenholtz, Shie Mannor:
Representation-Driven Reinforcement Learning. ICML 2023: 25588-25603 - [c10]Guy Tennenholtz, Nadav Merlis, Lior Shani, Martin Mladenov, Craig Boutilier:
Reinforcement Learning with History Dependent Dynamic Contexts. ICML 2023: 34011-34053 - [i23]Guy Tennenholtz, Nadav Merlis, Lior Shani, Martin Mladenov, Craig Boutilier:
Reinforcement Learning with History-Dependent Dynamic Contexts. CoRR abs/2302.02061 (2023) - [i22]Guy Tennenholtz, Martin Mladenov, Nadav Merlis, Craig Boutilier:
Ranking with Popularity Bias: User Welfare under Self-Amplification Dynamics. CoRR abs/2305.18333 (2023) - [i21]Ofir Nabati, Guy Tennenholtz, Shie Mannor:
Representation-Driven Reinforcement Learning. CoRR abs/2305.19922 (2023) - [i20]Alizée Pace, Hugo Yèche, Bernhard Schölkopf, Gunnar Rätsch, Guy Tennenholtz:
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding. CoRR abs/2306.01157 (2023) - [i19]Mohammad Ghavamzadeh, Marek Petrik, Guy Tennenholtz:
A Convex Relaxation Approach to Bayesian Regret Minimization in Offline Bandits. CoRR abs/2306.01237 (2023) - [i18]Craig Boutilier, Martin Mladenov, Guy Tennenholtz:
Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models. CoRR abs/2309.06375 (2023) - [i17]Guy Tennenholtz, Yinlam Chow, Chih-Wei Hsu, Jihwan Jeong, Lior Shani, Azamat Tulepbergenov, Deepak Ramachandran, Martin Mladenov, Craig Boutilier:
Demystifying Embedding Spaces using Large Language Models. CoRR abs/2310.04475 (2023) - [i16]Jihwan Jeong, Yinlam Chow, Guy Tennenholtz, Chih-Wei Hsu, Azamat Tulepbergenov, Mohammad Ghavamzadeh, Craig Boutilier:
Factual and Personalized Recommendations using Language Models and Reinforcement Learning. CoRR abs/2310.06176 (2023) - [i15]Li Ding, Masrour Zoghi, Guy Tennenholtz, Maryam Karimzadehgan:
Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation. CoRR abs/2310.18893 (2023) - 2022
- [c9]Roy Zohar, Shie Mannor, Guy Tennenholtz:
Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning. AAAI 2022: 9278-9285 - [c8]Guy Tennenholtz, Assaf Hallak, Gal Dalal, Shie Mannor, Gal Chechik, Uri Shalit:
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning. ICLR 2022 - [c7]Guy Tennenholtz, Shie Mannor:
Uncertainty Estimation Using Riemannian Model Dynamics for Offline Reinforcement Learning. NeurIPS 2022 - [c6]Guy Tennenholtz, Nadav Merlis, Lior Shani, Shie Mannor, Uri Shalit, Gal Chechik, Assaf Hallak, Gal Dalal:
Reinforcement Learning with a Terminator. NeurIPS 2022 - [i14]Guy Tennenholtz, Nadav Merlis, Lior Shani, Shie Mannor, Uri Shalit, Gal Chechik, Assaf Hallak, Gal Dalal:
Reinforcement Learning with a Terminator. CoRR abs/2205.15376 (2022) - 2021
- [c5]Nir Baram, Guy Tennenholtz, Shie Mannor:
Action redundancy in reinforcement learning. UAI 2021: 376-385 - [c4]Guy Tennenholtz, Uri Shalit, Shie Mannor, Yonathan Efroni:
Bandits with partially observable confounded data. UAI 2021: 430-439 - [i13]Guy Tennenholtz, Nir Baram, Shie Mannor:
GELATO: Geometrically Enriched Latent Model for Offline Reinforcement Learning. CoRR abs/2102.11327 (2021) - [i12]Nir Baram, Guy Tennenholtz, Shie Mannor:
Action Redundancy in Reinforcement Learning. CoRR abs/2102.11329 (2021) - [i11]Nir Baram, Guy Tennenholtz, Shie Mannor:
Maximum Entropy Reinforcement Learning with Mixture Policies. CoRR abs/2103.10176 (2021) - [i10]Roy Zohar, Shie Mannor, Guy Tennenholtz:
Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning. CoRR abs/2109.10632 (2021) - [i9]Guy Tennenholtz, Assaf Hallak, Gal Dalal, Shie Mannor, Gal Chechik, Uri Shalit:
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning. CoRR abs/2110.06539 (2021) - 2020
- [c3]Guy Tennenholtz, Uri Shalit, Shie Mannor:
Off-Policy Evaluation in Partially Observable Environments. AAAI 2020: 10276-10283 - [i8]Guy Tennenholtz, Uri Shalit, Shie Mannor, Yonathan Efroni:
Bandits with Partially Observable Offline Data. CoRR abs/2006.06731 (2020) - [i7]Asaf B. Cassel, Shie Mannor, Guy Tennenholtz:
The Pendulum Arrangement: Maximizing the Escape Time of Heterogeneous Random Walks. CoRR abs/2007.13232 (2020)
2010 – 2019
- 2019
- [c2]Guy Tennenholtz, Shie Mannor:
The Natural Language of Actions. ICML 2019: 6196-6205 - [c1]Chen Tessler, Guy Tennenholtz, Shie Mannor:
Distributional Policy Optimization: An Alternative Approach for Continuous Control. NeurIPS 2019: 1350-1360 - [i6]Guy Tennenholtz, Shie Mannor:
The Natural Language of Actions. CoRR abs/1902.01119 (2019) - [i5]Chen Tessler, Guy Tennenholtz, Shie Mannor:
Distributional Policy Optimization: An Alternative Approach for Continuous Control. CoRR abs/1905.09855 (2019) - [i4]Guy Tennenholtz, Shie Mannor, Uri Shalit:
Off-Policy Evaluation in Partially Observable Environments. CoRR abs/1909.03739 (2019) - [i3]Erez Schwartz, Guy Tennenholtz, Chen Tessler, Shie Mannor:
Natural Language State Representation for Reinforcement Learning. CoRR abs/1910.02789 (2019) - 2018
- [i2]Guy Tennenholtz, Tom Zahavy, Shie Mannor:
Train on Validation: Squeezing the Data Lemon. CoRR abs/1802.05846 (2018) - 2017
- [i1]Guy Tennenholtz, Constantine Caramanis, Shie Mannor:
The Stochastic Firefighter Problem. CoRR abs/1711.08237 (2017)
Coauthor Index
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last updated on 2024-09-04 00:32 CEST by the dblp team
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