Guy Tennenholtz

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 41

Top Topics

Reinforcement Learning

Maximum Entropy

Top Venues

Publications

Craig Boutilier, Martin Mladenov, Guy Tennenholtz
Recommender Ecosystems: A Mechanism Design Perspective on Holistic Modeling and Optimization. AAAI (2024)
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)
Alizée Pace, Hugo Yèche, Bernhard Schölkopf, Gunnar Rätsch, Guy Tennenholtz
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding. ICLR (2024)
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 (2024)
Guy Tennenholtz, Yinlam Chow, Chih-Wei Hsu, Lior Shani, Ethan Liang, Craig Boutilier
Embedding-Aligned Language Models. CoRR (2024)
Ori Linial, Guy Tennenholtz, Uri Shalit
Benchmarks for Reinforcement Learning with Biased Offline Data and Imperfect Simulators. CoRR (2024)
Ofir Nabati, Guy Tennenholtz, Shie Mannor
Representation-Driven Reinforcement Learning. CoRR (2023)
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 (2023)
Guy Tennenholtz, Martin Mladenov, Nadav Merlis, Craig Boutilier
Ranking with Popularity Bias: User Welfare under Self-Amplification Dynamics. CoRR (2023)
Pranav Khanna, Guy Tennenholtz, Nadav Merlis, Shie Mannor, Chen Tessler
Never Worse, Mostly Better: Stable Policy Improvement in Deep Reinforcement Learning. AAMAS (2023)
Guy Tennenholtz, Nadav Merlis, Lior Shani, Martin Mladenov, Craig Boutilier
Reinforcement Learning with History-Dependent Dynamic Contexts. CoRR (2023)
Ofir Nabati, Guy Tennenholtz, Shie Mannor
Representation-Driven Reinforcement Learning. ICML (2023)
Mohammad Ghavamzadeh, Marek Petrik, Guy Tennenholtz
A Convex Relaxation Approach to Bayesian Regret Minimization in Offline Bandits. CoRR (2023)
Alizée Pace, Hugo Yèche, Bernhard Schölkopf, Gunnar Rätsch, Guy Tennenholtz
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding. CoRR (2023)
Craig Boutilier, Martin Mladenov, Guy Tennenholtz
Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models. CoRR (2023)
Guy Tennenholtz, Nadav Merlis, Lior Shani, Martin Mladenov, Craig Boutilier
Reinforcement Learning with History Dependent Dynamic Contexts. ICML (2023)
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 (2023)
Li Ding, Masrour Zoghi, Guy Tennenholtz, Maryam Karimzadehgan
Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation. CoRR (2023)
Guy Tennenholtz, Shie Mannor
Uncertainty Estimation Using Riemannian Model Dynamics for Offline Reinforcement Learning. NeurIPS (2022)
Guy Tennenholtz, Nadav Merlis, Lior Shani, Shie Mannor, Uri Shalit, Gal Chechik, Assaf Hallak, Gal Dalal
Reinforcement Learning with a Terminator. NeurIPS (2022)
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)
Roy Zohar, Shie Mannor, Guy Tennenholtz
Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning. AAAI (2022)
Guy Tennenholtz, Nadav Merlis, Lior Shani, Shie Mannor, Uri Shalit, Gal Chechik, Assaf Hallak, Gal Dalal
Reinforcement Learning with a Terminator. CoRR (2022)
Guy Tennenholtz, Nir Baram, Shie Mannor
GELATO: Geometrically Enriched Latent Model for Offline Reinforcement Learning. CoRR (2021)
Nir Baram, Guy Tennenholtz, Shie Mannor
Maximum Entropy Reinforcement Learning with Mixture Policies. CoRR (2021)
Roy Zohar, Shie Mannor, Guy Tennenholtz
Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning. CoRR (2021)
Guy Tennenholtz, Assaf Hallak, Gal Dalal, Shie Mannor, Gal Chechik, Uri Shalit
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning. CoRR (2021)
Nir Baram, Guy Tennenholtz, Shie Mannor
Action Redundancy in Reinforcement Learning. CoRR (2021)
Nir Baram, Guy Tennenholtz, Shie Mannor
Action redundancy in reinforcement learning. UAI (2021)
Guy Tennenholtz, Uri Shalit, Shie Mannor, Yonathan Efroni
Bandits with partially observable confounded data. UAI (2021)
Guy Tennenholtz, Uri Shalit, Shie Mannor
Off-Policy Evaluation in Partially Observable Environments. AAAI (2020)
Guy Tennenholtz, Uri Shalit, Shie Mannor, Yonathan Efroni
Bandits with Partially Observable Offline Data. CoRR (2020)
Asaf Cassel, Shie Mannor, Guy Tennenholtz
The Pendulum Arrangement: Maximizing the Escape Time of Heterogeneous Random Walks. CoRR (2020)
Chen Tessler, Guy Tennenholtz, Shie Mannor
Distributional Policy Optimization: An Alternative Approach for Continuous Control. CoRR (2019)
Chen Tessler, Guy Tennenholtz, Shie Mannor
Distributional Policy Optimization: An Alternative Approach for Continuous Control. NeurIPS (2019)
Guy Tennenholtz, Shie Mannor, Uri Shalit
Off-Policy Evaluation in Partially Observable Environments. CoRR (2019)
Guy Tennenholtz, Shie Mannor
The Natural Language of Actions. ICML (2019)
Erez Schwartz, Guy Tennenholtz, Chen Tessler, Shie Mannor
Natural Language State Representation for Reinforcement Learning. CoRR (2019)
Guy Tennenholtz, Shie Mannor
The Natural Language of Actions. CoRR (2019)
Guy Tennenholtz, Tom Zahavy, Shie Mannor
Train on Validation: Squeezing the Data Lemon. CoRR (2018)
Guy Tennenholtz, Constantine Caramanis, Shie Mannor
The Stochastic Firefighter Problem. CoRR (2017)