1 citations · 2 across the 5 of their papers we have counts for
7 papers
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…
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…
Tight Approximation Algorithms for Two Dimensional Guillotine Strip Packing
Arindam Khan, Aditya Lonkar, Arnab Maiti +2
In the Strip Packing problem (SP), we are given a vertical half-strip and a set of axis-aligned rectangles of width at most . The goal is to find a n…
Universal and Tight Online Algorithms for Generalized-Mean Welfare
Siddharth Barman, Arindam Khan, Arnab Maiti
We study fair and efficient allocation of divisible goods, in an online manner, among agents. The goods arrive online in a sequence of time periods. The agents' values for…
On Guillotine Separable Packings for the Two-dimensional Geometric Knapsack Problem
Arindam Khan, Arnab Maiti, Amatya Sharma +1
In two-dimensional geometric knapsack problem, we are given a set of n axis-aligned rectangular items and an axis-aligned square-shaped knapsack. Each item has integral width, inte…
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…