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ORCIDNadav Merlis
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2020 – today
- 2024
[c13]Dorian Baudry, Nadav Merlis, Mathieu Benjamin Molina, Hugo Richard, Vianney Perchet:
Multi-armed bandits with guaranteed revenue per arm. AISTATS 2024: 379-387- [i17]Nadav Merlis, Dorian Baudry, Vianney Perchet:
The Value of Reward Lookahead in Reinforcement Learning. CoRR abs/2403.11637 (2024) - [i16]Itai Shufaro, Nadav Merlis, Nir Weinberger, Shie Mannor:
On Bits and Bandits: Quantifying the Regret-Information Trade-off. CoRR abs/2405.16581 (2024) - [i15]Nadav Merlis:
Reinforcement Learning with Lookahead Information. CoRR abs/2406.02258 (2024) - [i14]Matilde Tullii, Solenne Gaucher, Nadav Merlis, Vianney Perchet:
Improved Algorithms for Contextual Dynamic Pricing. CoRR abs/2406.11316 (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]Nadav Merlis, Hugo Richard, Flore Sentenac, Corentin Odic, Mathieu Molina, Vianney Perchet:
On Preemption and Learning in Stochastic Scheduling. ICML 2023: 24478-24516 - [c10]Guy Tennenholtz, Nadav Merlis, Lior Shani, Martin Mladenov, Craig Boutilier:
Reinforcement Learning with History Dependent Dynamic Contexts. ICML 2023: 34011-34053 - [i13]Guy Tennenholtz, Nadav Merlis, Lior Shani, Martin Mladenov, Craig Boutilier:
Reinforcement Learning with History-Dependent Dynamic Contexts. CoRR abs/2302.02061 (2023) - [i12]Guy Tennenholtz, Martin Mladenov, Nadav Merlis, Craig Boutilier:
Ranking with Popularity Bias: User Welfare under Self-Amplification Dynamics. CoRR abs/2305.18333 (2023) - 2022
- [c9]Guy Tennenholtz, Nadav Merlis, Lior Shani, Shie Mannor, Uri Shalit, Gal Chechik, Assaf Hallak, Gal Dalal:
Reinforcement Learning with a Terminator. NeurIPS 2022 - [i11]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
- [c8]Yonathan Efroni, Nadav Merlis, Shie Mannor:
Reinforcement Learning with Trajectory Feedback. AAAI 2021: 7288-7295 - [c7]Nadav Merlis, Shie Mannor:
Lenient Regret for Multi-Armed Bandits. AAAI 2021: 8950-8957 - [c6]Yonathan Efroni, Nadav Merlis, Aadirupa Saha, Shie Mannor:
Confidence-Budget Matching for Sequential Budgeted Learning. ICML 2021: 2937-2947 - [c5]Oren Peer, Chen Tessler, Nadav Merlis, Ron Meir:
Ensemble Bootstrapping for Q-Learning. ICML 2021: 8454-8463 - [i10]Yonathan Efroni, Nadav Merlis, Aadirupa Saha, Shie Mannor:
Confidence-Budget Matching for Sequential Budgeted Learning. CoRR abs/2102.03400 (2021) - [i9]Oren Peer, Chen Tessler, Nadav Merlis, Ron Meir:
Ensemble Bootstrapping for Q-Learning. CoRR abs/2103.00445 (2021) - [i8]Nadav Merlis, Yonathan Efroni, Shie Mannor:
Dare not to Ask: Problem-Dependent Guarantees for Budgeted Bandits. CoRR abs/2110.05724 (2021) - 2020
- [c4]Nadav Merlis, Shie Mannor:
Tight Lower Bounds for Combinatorial Multi-Armed Bandits. COLT 2020: 2830-2857 - [i7]Nadav Merlis, Shie Mannor:
Tight Lower Bounds for Combinatorial Multi-Armed Bandits. CoRR abs/2002.05392 (2020) - [i6]Nadav Merlis, Shie Mannor:
Lenient Regret for Multi-Armed Bandits. CoRR abs/2008.03959 (2020) - [i5]Yonathan Efroni, Nadav Merlis, Shie Mannor:
Reinforcement Learning with Trajectory Feedback. CoRR abs/2008.06036 (2020)
2010 – 2019
- 2019
- [c3]Nadav Merlis, Shie Mannor:
Batch-Size Independent Regret Bounds for the Combinatorial Multi-Armed Bandit Problem. COLT 2019: 2465-2489 - [c2]Yonathan Efroni, Nadav Merlis, Mohammad Ghavamzadeh, Shie Mannor:
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies. NeurIPS 2019: 12203-12213 - [i4]Nadav Merlis, Shie Mannor:
Batch-Size Independent Regret Bounds for the Combinatorial Multi-Armed Bandit Problem. CoRR abs/1905.03125 (2019) - [i3]Yonathan Efroni, Nadav Merlis, Mohammad Ghavamzadeh, Shie Mannor:
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies. CoRR abs/1905.11527 (2019) - [i2]Chen Tessler, Nadav Merlis, Shie Mannor:
Stabilizing Off-Policy Reinforcement Learning with Conservative Policy Gradients. CoRR abs/1910.01062 (2019) - 2018
- [c1]Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J. Mankowitz, Shie Mannor:
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning. NeurIPS 2018: 3566-3577 - [i1]Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J. Mankowitz, Shie Mannor:
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning. CoRR abs/1809.02121 (2018)
Coauthor Index
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