Tennenholtz, Guy

25 publications

ICLR 2026 Spectral Bellman Method: Unifying Representation and Exploration in RL Ofir Nabati, Bo Dai, Shie Mannor, Guy Tennenholtz

ICLR 2025 Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models Yinlam Chow, Guy Tennenholtz, Izzeddin Gur, Vincent Zhuang, Bo Dai, Aviral Kumar, Rishabh Agarwal, Sridhar Thiagarajan, Craig Boutilier, Aleksandra Faust

ICML 2025 Preference Adaptive and Sequential Text-to-Image Generation Ofir Nabati, Guy Tennenholtz, Chihwei Hsu, Moonkyung Ryu, Deepak Ramachandran, Yinlam Chow, Xiang Li, Craig Boutilier

ICML 2024 Bayesian Regret Minimization in Offline Bandits Marek Petrik, Guy Tennenholtz, Mohammad Ghavamzadeh

ICLR 2024 Delphic Offline Reinforcement Learning Under Nonidentifiable Hidden Confounding Alizée Pace, Hugo Yèche, Bernhard Schölkopf, Gunnar Ratsch, Guy Tennenholtz

ICLR 2024 Demystifying Embedding Spaces Using Large Language Models Guy Tennenholtz, Yinlam Chow, ChihWei Hsu, Jihwan Jeong, Lior Shani, Azamat Tulepbergenov, Deepak Ramachandran, Martin Mladenov, Craig Boutilier

NeurIPS 2024 DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning Anthony Liang, Guy Tennenholtz, Chih-Wei Hsu, Yinlam Chow, Erdem Biyik, Craig Boutilier

ICMLW 2024 DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning Anthony Liang, Guy Tennenholtz, ChihWei Hsu, Yinlam Chow, Erdem Biyik, Craig Boutilier

NeurIPS 2024 Embedding-Aligned Language Models Guy Tennenholtz, Yinlam Chow, Chih-Wei Hsu, Lior Shani, Ethan Liang, Craig Boutilier

AAAI 2024 Recommender Ecosystems: A Mechanism Design Perspective on Holistic Modeling and Optimization Craig Boutilier, Martin Mladenov, Guy Tennenholtz

ICMLW 2023 Delphic Offline Reinforcement Learning Under Nonidentifiable Hidden Confounding Alizée Pace, Hugo Yèche, Bernhard Schölkopf, Gunnar Ratsch, Guy Tennenholtz

NeurIPSW 2023 Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation Li Ding, Masrour Zoghi, Guy Tennenholtz, Maryam Karimzadehgan

ICML 2023 Reinforcement Learning with History Dependent Dynamic Contexts Guy Tennenholtz, Nadav Merlis, Lior Shani, Martin Mladenov, Craig Boutilier

ICML 2023 Representation-Driven Reinforcement Learning Ofir Nabati, Guy Tennenholtz, Shie Mannor

AAAI 2022 Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning Roy Zohar, Shie Mannor, Guy Tennenholtz

ICLR 2022 On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning Guy Tennenholtz, Assaf Hallak, Gal Dalal, Shie Mannor, Gal Chechik, Uri Shalit

NeurIPS 2022 Reinforcement Learning with a Terminator Guy Tennenholtz, Nadav Merlis, Lior Shani, Shie Mannor, Uri Shalit, Gal Chechik, Assaf Hallak, Gal Dalal

NeurIPS 2022 Uncertainty Estimation Using Riemannian Model Dynamics for Offline Reinforcement Learning Guy Tennenholtz, Shie Mannor

UAI 2021 Action Redundancy in Reinforcement Learning Nir Baram, Guy Tennenholtz, Shie Mannor

UAI 2021 Bandits with Partially Observable Confounded Data Guy Tennenholtz, Uri Shalit, Shie Mannor, Yonathan Efroni

NeurIPSW 2021 Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning Guy Tennenholtz, Assaf Hallak, Gal Dalal, Shie Mannor, Gal Chechik, Uri Shalit

NeurIPSW 2021 Latent Geodesics of Model Dynamics for Offline Reinforcement Learning Guy Tennenholtz, Nir Baram, Shie Mannor

AAAI 2020 Off-Policy Evaluation in Partially Observable Environments Guy Tennenholtz, Uri Shalit, Shie Mannor

NeurIPS 2019 Distributional Policy Optimization: An Alternative Approach for Continuous Control Chen Tessler, Guy Tennenholtz, Shie Mannor

ICML 2019 The Natural Language of Actions Guy Tennenholtz, Shie Mannor