| 2026 | AAAI | Online Linear Regression with Paid Stochastic Features. | Nadav Merlis, Kyoungseok Jang, Nicol Cesa-Bianchi |
| 2025 | ICLR | On Bits and Bandits: Quantifying the Regret-Information Trade-off. | Itai Shufaro, Nadav Merlis, Nir Weinberger, Shie Mannor |
| 2024 | AISTATS | Multi-armed bandits with guaranteed revenue per arm. | Dorian Baudry, Nadav Merlis, Mathieu Benjamin Molina, Hugo Richard, Vianney Perchet |
| 2023 | ICML | On Preemption and Learning in Stochastic Scheduling. | Nadav Merlis, Hugo Richard, Flore Sentenac, Corentin Odic, Mathieu Molina, Vianney Perchet |
| 2023 | ICML | Reinforcement Learning with History Dependent Dynamic Contexts. | Guy Tennenholtz, Nadav Merlis, Lior Shani, Martin Mladenov, Craig Boutilier |
| 2021 | AAAI | Reinforcement Learning with Trajectory Feedback. | Yonathan Efroni, Nadav Merlis, Shie Mannor |
| 2021 | AAAI | Lenient Regret for Multi-Armed Bandits. | Nadav Merlis, Shie Mannor |
| 2021 | ICML | Confidence-Budget Matching for Sequential Budgeted Learning. | Yonathan Efroni, Nadav Merlis, Aadirupa Saha, Shie Mannor |
| 2021 | ICML | Ensemble Bootstrapping for Q-Learning. | Oren Peer, Chen Tessler, Nadav Merlis, Ron Meir |
| 2020 | COLT | Tight Lower Bounds for Combinatorial Multi-Armed Bandits. | Nadav Merlis, Shie Mannor |
| 2019 | COLT | Batch-Size Independent Regret Bounds for the Combinatorial Multi-Armed Bandit Problem. | Nadav Merlis, Shie Mannor |