| 2026 | COLT | Adversarial Learning in Games with Bandit Feedback: Logarithmic Pure-Strategy Maximin Regret. | Shinji Ito, Haipeng Luo, Arnab Maiti, Taira Tsuchiya, Yue Wu |
| 2026 | COLT | On the Power of Adaptivity for ε-Best Arm Identification in Linear Bandits. | Arnab Maiti, Yunbei Xu, Kevin Jamieson |
| 2025 | COLT | Open Problem: Optimal Instance-Dependent Sample Complexity for finding Nash Equilibrium in Two Player Zero-Sum Matrix games. | Arnab Maiti |
| 2025 | COLT | Efficient Near-Optimal Algorithm for Online Shortest Paths in Directed Acyclic Graphs with Bandit Feedback Against Adaptive Adversaries. | Arnab Maiti, Zhiyuan Fan, Kevin Jamieson, Lillian J. Ratliff, Gabriele Farina |
| 2025 | ICML | Learning to Incentivize in Repeated Principal-Agent Problems with Adversarial Agent Arrivals. | Junyan Liu, Arnab Maiti, Artin Tajdini, Kevin Jamieson, Lillian J. Ratliff |
| 2025 | SAGT | On the Limitations and Possibilities of Nash Regret Minimization in Zero-Sum Matrix Games Under Noisy Feedback. | Arnab Maiti, Kevin Jamieson, Lillian J. Ratliff |
| 2024 | AISTATS | Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits & Dueling Bandits. | Arnab Maiti, Ross Boczar, Kevin Jamieson, Lillian J. Ratliff |
| 2024 | LATIN | On Binary Networked Public Goods Game with Altruism. | Arnab Maiti, Palash Dey |
| 2023 | AAAI | Fairness and Welfare Quantification for Regret in Multi-Armed Bandits. | Siddharth Barman, Arindam Khan, Arnab Maiti, Ayush Sawarni |
| 2023 | AISTATS | Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games. | Arnab Maiti, Kevin Jamieson, Lillian J. Ratliff |
| 2022 | AAAI | Universal and Tight Online Algorithms for Generalized-Mean Welfare. | Siddharth Barman, Arindam Khan, Arnab Maiti |
| 2022 | ICALP | Tight Approximation Algorithms for Two-Dimensional Guillotine Strip Packing. | Arindam Khan, Aditya Lonkar, Arnab Maiti, Amatya Sharma, Andreas Wiese |
| 2022 | IJCAI | Parameterized Algorithms for Kidney Exchange. | Arnab Maiti, Palash Dey |