Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning. - Tom Zahavy; Matan Haroush; Nadav Merlis; Daniel J. Mankowitz; Shie Mannor (NeurIPS, 2018)

Batch-Size Independent Regret Bounds for the Combinatorial Multi-Armed Bandit Problem. - Nadav Merlis; Shie Mannor (COLT, 2019)

Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies. - Yonathan Efroni; Nadav Merlis; Mohammad Ghavamzadeh; Shie Mannor (NeurIPS, 2019)

Stabilizing Off-Policy Reinforcement Learning with Conservative Policy Gradients. - Chen Tessler; Nadav Merlis; Shie Mannor (CoRR, 2019)

Tight Lower Bounds for Combinatorial Multi-Armed Bandits. - Nadav Merlis; Shie Mannor (COLT, 2020)

Reinforcement Learning with Trajectory Feedback. - Yonathan Efroni; Nadav Merlis; Shie Mannor (AAAI, 2021)

Lenient Regret for Multi-Armed Bandits. - Nadav Merlis; Shie Mannor (AAAI, 2021)

Confidence-Budget Matching for Sequential Budgeted Learning. - Yonathan Efroni; Nadav Merlis; Aadirupa Saha; Shie Mannor (ICML, 2021)

Ensemble Bootstrapping for Q-Learning. - Oren Peer; Chen Tessler; Nadav Merlis; Ron Meir (ICML, 2021)

Dare not to Ask: Problem-Dependent Guarantees for Budgeted Bandits. - Nadav Merlis; Yonathan Efroni; Shie Mannor (CoRR, 2021)

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)

On Preemption and Learning in Stochastic Scheduling. - Nadav Merlis; Hugo Richard; Flore Sentenac; Corentin Odic; Mathieu Molina; Vianney Perchet (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)

Multi-armed bandits with guaranteed revenue per arm. - Dorian Baudry; Nadav Merlis; Mathieu Benjamin Molina; Hugo Richard; Vianney Perchet (AISTATS, 2024)

Reinforcement Learning with Lookahead Information. - Nadav Merlis (NeurIPS, 2024)

The Value of Reward Lookahead in Reinforcement Learning. - Nadav Merlis; Dorian Baudry; Vianney Perchet (NeurIPS, 2024)

Improved Algorithms for Contextual Dynamic Pricing. - Matilde Tullii; Solenne Gaucher; Nadav Merlis; Vianney Perchet (NeurIPS, 2024)

Stable Matching with Ties: Approximation Ratios and Learning. - Shiyun Lin; Simon Mauras; Nadav Merlis; Vianney Perchet (CoRR, 2024)

On Bits and Bandits: Quantifying the Regret-Information Trade-off. - Itai Shufaro; Nadav Merlis; Nir Weinberger; Shie Mannor (ICLR, 2025)