1 citations · 3 across the 19 of their papers we have counts for
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On the Power of Adaptivity for -Best Arm Identification in Linear Bandits
Arnab Maiti, Yunbei Xu, Kevin Jamieson
We study the minimax sample complexity of -best arm identification in linear bandits. Given a compact action set that spans and an unknown…
Adversarial Learning in Games with Bandit Feedback: Logarithmic Pure-Strategy Maximin Regret
Shinji Ito, Haipeng Luo, Arnab Maiti +2
Learning to play zero-sum games is a fundamental problem in game theory and machine learning. While significant progress has been made in minimizing external regret in the self-pla…
Adapting to Stochastic and Adversarial Losses in Episodic MDPs with Aggregate Bandit Feedback
Shinji Ito, Kevin Jamieson, Haipeng Luo +2
We study online learning in finite-horizon episodic Markov decision processes (MDPs) under the challenging aggregate bandit feedback model, where the learner observes only the cumu…
On the Universal Near Optimality of Hedge in Combinatorial Settings
Zhiyuan Fan, Arnab Maiti, Kevin Jamieson +2
In this paper, we study the classical Hedge algorithm in combinatorial settings. In each round, the learner selects a vector from a set ,…
Efficient Near-Optimal Algorithm for Online Shortest Paths in Directed Acyclic Graphs with Bandit Feedback Against Adaptive Adversaries
Arnab Maiti, Zhiyuan Fan, Kevin Jamieson +2
In this paper, we study the online shortest path problem in directed acyclic graphs (DAGs) under bandit feedback against an adaptive adversary. Given a DAG with a sour…
Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits & Dueling Bandits
Arnab Maiti, Ross Boczar, Kevin Jamieson +1
We study the sample complexity of identifying the pure strategy Nash equilibrium (PSNE) in a two-player zero-sum matrix game with noise. Formally, we are given a stochastic model w…
On the Limitations and Possibilities of Nash Regret Minimization in Zero-Sum Matrix Games under Noisy Feedback
Arnab Maiti, Kevin Jamieson, Lillian J. Ratliff
This paper studies a variant of two-player zero-sum matrix games, where, at each timestep, the row player selects row , the column player selects column , and the row player…
Fairness and Welfare Quantification for Regret in Multi-Armed Bandits
Siddharth Barman, Arindam Khan, Arnab Maiti +1
We extend the notion of regret with a welfarist perspective. Focussing on the classic multi-armed bandit (MAB) framework, the current work quantifies the performance of bandit algo…
Streaming Algorithms for Stochastic Multi-armed Bandits
Arnab Maiti, Vishakha Patil, Arindam Khan
We study the Stochastic Multi-armed Bandit problem under bounded arm-memory. In this setting, the arms arrive in a stream, and the number of arms that can be stored in the memory a…