The Stochastic Firefighter Problem. - Guy Tennenholtz; Constantine Caramanis; Shie Mannor (CoRR, 2017)

Train on Validation: Squeezing the Data Lemon. - Guy Tennenholtz; Tom Zahavy; Shie Mannor (CoRR, 2018)

The Natural Language of Actions. - Guy Tennenholtz; Shie Mannor (ICML, 2019)

Distributional Policy Optimization: An Alternative Approach for Continuous Control. - Chen Tessler; Guy Tennenholtz; Shie Mannor (NeurIPS, 2019)

Natural Language State Representation for Reinforcement Learning. - Erez Schwartz; Guy Tennenholtz; Chen Tessler; Shie Mannor (CoRR, 2019)

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

Bandits with Partially Observable Offline Data. - Guy Tennenholtz; Uri Shalit; Shie Mannor; Yonathan Efroni (CoRR, 2020)

The Pendulum Arrangement: Maximizing the Escape Time of Heterogeneous Random Walks. - Asaf B. Cassel; Shie Mannor; Guy Tennenholtz (CoRR, 2020)

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 (UAI, 2021)

GELATO: Geometrically Enriched Latent Model for Offline Reinforcement Learning. - Guy Tennenholtz; Nir Baram; Shie Mannor (CoRR, 2021)

Maximum Entropy Reinforcement Learning with Mixture Policies. - Nir Baram; Guy Tennenholtz; Shie Mannor (CoRR, 2021)

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

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

Uncertainty Estimation Using Riemannian Model Dynamics for Offline Reinforcement Learning. - Guy Tennenholtz; Shie Mannor (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)

Never Worse, Mostly Better: Stable Policy Improvement in Deep Reinforcement Learning. - Pranav Khanna; Guy Tennenholtz; Nadav Merlis; Shie Mannor; Chen Tessler (AAMAS, 2023)

Representation-Driven Reinforcement Learning. - Ofir Nabati; Guy Tennenholtz; Shie Mannor (ICML, 2023)

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

Ranking with Popularity Bias: User Welfare under Self-Amplification Dynamics. - Guy Tennenholtz; Martin Mladenov; Nadav Merlis; Craig Boutilier (CoRR, 2023)

A Convex Relaxation Approach to Bayesian Regret Minimization in Offline Bandits. - Mohammad Ghavamzadeh; Marek Petrik; Guy Tennenholtz (CoRR, 2023)

Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models. - Craig Boutilier; Martin Mladenov; Guy Tennenholtz (CoRR, 2023)

Factual and Personalized Recommendations using Language Models and Reinforcement Learning. - Jihwan Jeong; Yinlam Chow; Guy Tennenholtz; Chih-Wei Hsu; Azamat Tulepbergenov; Mohammad Ghavamzadeh; Craig Boutilier (CoRR, 2023)

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

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

Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding. - Alizée Pace; Hugo Yèche; Bernhard Schölkopf; Gunnar Rätsch; Guy Tennenholtz (ICLR, 2024)

Demystifying Embedding Spaces using Large Language Models. - Guy Tennenholtz; Yinlam Chow; Chih-Wei Hsu; Jihwan Jeong; Lior Shani; Azamat Tulepbergenov; Deepak Ramachandran; Martin Mladenov; Craig Boutilier (ICLR, 2024)

Bayesian Regret Minimization in Offline Bandits. - Marek Petrik; Guy Tennenholtz; Mohammad Ghavamzadeh (ICML, 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 (NeurIPS, 2024)

Embedding-Aligned Language Models. - Guy Tennenholtz; Yinlam Chow; Chih-Wei Hsu; Lior Shani; Yi Liang; Craig Boutilier (NeurIPS, 2024)

Embedding-Aligned Language Models. - Guy Tennenholtz; Yinlam Chow; Chih-Wei Hsu; Lior Shani; Ethan Liang; Craig Boutilier (CoRR, 2024)

Benchmarks for Reinforcement Learning with Biased Offline Data and Imperfect Simulators. - Ori Linial; Guy Tennenholtz; Uri Shalit (CoRR, 2024)

Personalized and Sequential Text-to-Image Generation. - Ofir Nabati; Guy Tennenholtz; Chih-Wei Hsu; Moonkyung Ryu; Deepak Ramachandran; Yinlam Chow; Xiang Li; Craig Boutilier (CoRR, 2024)

Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models. - Yinlam Chow; Guy Tennenholtz; Izzeddin Gur; Vincent Zhuang; Bo Dai 001; Aviral Kumar; Rishabh Agarwal; Sridhar Thiagarajan; Craig Boutilier; Aleksandra Faust (ICLR, 2025)

Descriptive History Representations: Learning Representations by Answering Questions. - Guy Tennenholtz; Jihwan Jeong; Chih-Wei Hsu; Yinlam Chow; Craig Boutilier (CoRR, 2025)

Spectral Bellman Method: Unifying Representation and Exploration in RL. - Ofir Nabati; Bo Dai 001; Shie Mannor; Guy Tennenholtz (CoRR, 2025)