1 citations · 3 across the 9 of their papers we have counts for
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Near-Optimal Regret Bounds for Monotone Stochastic Optimization under Semi-Bandit Feedback
Tao Hu, Rohan Ghuge
Stochastic optimization models sequential decision-making under uncertainty, typically assuming known input distributions. When these distributions are unknown, \citet{agarwal2026s…
Improved and Oracle-Efficient Online -Multicalibration
Rohan Ghuge, Vidya Muthukumar, Sahil Singla
We study \emph{online multicalibration}, a framework for ensuring calibrated predictions across multiple groups in adversarial settings, across rounds. Although online calibrat…
Semi-Bandit Learning for Monotone Stochastic Optimization
Arpit Agarwal, Rohan Ghuge, Viswanath Nagarajan +1
Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables. Exact or approximation algor…
An Asymptotically Optimal Batched Algorithm for the Dueling Bandit Problem
Arpit Agarwal, Rohan Ghuge, Viswanath Nagarajan
We study the -armed dueling bandit problem, a variation of the traditional multi-armed bandit problem in which feedback is obtained in the form of pairwise comparisons. Previous…
Batched Dueling Bandits
Arpit Agarwal, Rohan Ghuge, Viswanath Nagarajan
The -armed dueling bandit problem, where the feedback is in the form of noisy pairwise comparisons, has been widely studied. Previous works have only focused on the sequential s…