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Arnab Maiti
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2020 – today
- 2024
- [j2]Arnab Maiti, Palash Dey:
On Parameterized Complexity of Binary Networked Public Goods Game. Algorithmica 86(1): 307-333 (2024) - [j1]Arnab Maiti, Palash Dey:
Query complexity of tournament solutions. Theor. Comput. Sci. 991: 114422 (2024) - [c12]Arnab Maiti, Ross Boczar, Kevin G. Jamieson, Lillian J. Ratliff:
Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits & Dueling Bandits. AISTATS 2024: 2602-2610 - [c11]Arnab Maiti, Palash Dey:
On Binary Networked Public Goods Game with Altruism. LATIN (2) 2024: 289-303 - 2023
- [c10]Siddharth Barman, Arindam Khan, Arnab Maiti, Ayush Sawarni:
Fairness and Welfare Quantification for Regret in Multi-Armed Bandits. AAAI 2023: 6762-6769 - [c9]Arnab Maiti, Kevin G. Jamieson, Lillian J. Ratliff:
Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games. AISTATS 2023: 9429-9469 - [i13]Arnab Maiti, Kevin G. Jamieson, Lillian J. Ratliff:
Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games. CoRR abs/2303.10565 (2023) - [i12]Arnab Maiti, Kevin G. Jamieson, Lillian J. Ratliff:
Logarithmic Regret for Matrix Games against an Adversary with Noisy Bandit Feedback. CoRR abs/2306.13233 (2023) - [i11]Arnab Maiti, Ross Boczar, Kevin G. Jamieson, Lillian J. Ratliff:
Query-Efficient Algorithms to Find the Unique Nash Equilibrium in a Two-Player Zero-Sum Matrix Game. CoRR abs/2310.16236 (2023) - [i10]Arnab Maiti, Ross Boczar, Kevin G. Jamieson, Lillian J. Ratliff:
Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits & Dueling Bandits. CoRR abs/2310.16252 (2023) - 2022
- [c8]Siddharth Barman, Arindam Khan, Arnab Maiti:
Universal and Tight Online Algorithms for Generalized-Mean Welfare. AAAI 2022: 4793-4800 - [c7]Arnab Maiti, Palash Dey:
On Parameterized Complexity of Binary Networked Public Goods Game. AAMAS 2022: 871-879 - [c6]Arnab Maiti, Palash Dey:
Parameterized Algorithms for Kidney Exchange. AAMAS 2022: 1693-1695 - [c5]Arindam Khan, Aditya Lonkar, Arnab Maiti, Amatya Sharma, Andreas Wiese:
Tight Approximation Algorithms for Two-Dimensional Guillotine Strip Packing. ICALP 2022: 80:1-80:20 - [c4]Arnab Maiti, Palash Dey:
Parameterized Algorithms for Kidney Exchange. IJCAI 2022: 405-411 - [i9]Arindam Khan, Aditya Lonkar, Arnab Maiti, Amatya Sharma, Andreas Wiese:
Tight Approximation Algorithms for Two Dimensional Guillotine Strip Packing. CoRR abs/2202.05989 (2022) - [i8]Arnab Maiti, Palash Dey:
On Binary Networked Public Goods Game with Altruism. CoRR abs/2205.00442 (2022) - [i7]Siddharth Barman, Arindam Khan, Arnab Maiti, Ayush Sawarni:
Fairness and Welfare Quantification for Regret in Multi-Armed Bandits. CoRR abs/2205.13930 (2022) - 2021
- [c3]Palash Dey, Arnab Maiti, Amatya Sharma:
On Parameterized Complexity of Liquid Democracy. CALDAM 2021: 83-94 - [c2]Arindam Khan, Arnab Maiti, Amatya Sharma, Andreas Wiese:
On Guillotine Separable Packings for the Two-Dimensional Geometric Knapsack Problem. SoCG 2021: 48:1-48:17 - [c1]Arnab Maiti, Vishakha Patil, Arindam Khan:
Multi-Armed Bandits with Bounded Arm-Memory: Near-Optimal Guarantees for Best-Arm Identification and Regret Minimization. NeurIPS 2021: 19553-19565 - [i6]Arindam Khan, Arnab Maiti, Amatya Sharma, Andreas Wiese:
On Guillotine Separable Packings for the Two-dimensional Geometric Knapsack Problem. CoRR abs/2103.09735 (2021) - [i5]Siddharth Barman, Arindam Khan, Arnab Maiti:
Universal and Tight Online Algorithms for Generalized-Mean Welfare. CoRR abs/2109.00874 (2021) - [i4]Arnab Maiti, Palash Dey:
Parameterized Algorithms for Kidney Exchange. CoRR abs/2112.10250 (2021) - 2020
- [i3]Palash Dey, Arnab Maiti, Amatya Sharma:
On Parameterized Complexity of Liquid Democracy. CoRR abs/2011.14192 (2020) - [i2]Arnab Maiti, Palash Dey:
On Parameterized Complexity of Binary Networked Public Goods Game. CoRR abs/2012.01880 (2020) - [i1]Arnab Maiti, Vishakha Patil, Arindam Khan:
Streaming Algorithms for Stochastic Multi-armed Bandits. CoRR abs/2012.05142 (2020)
Coauthor Index
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last updated on 2024-06-03 00:57 CEST by the dblp team
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