1 citations · 1 across the 2 of their papers we have counts for
11 papers
Online Learning and Bandits with Queried Hints
Aditya Bhaskara, Sreenivas Gollapudi, Sungjin Im +2
We consider the classic online learning and stochastic multi-armed bandit (MAB) problems, when at each step, the online policy can probe and find out which of a small number ()…
Logarithmic Regret from Sublinear Hints
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar +1
We consider the online linear optimization problem, where at every step the algorithm plays a point in the unit ball, and suffers loss for some cost…
Going Beyond Classification Accuracy Metrics in Model Compression
Vinu Joseph, Shoaib Ahmed Siddiqui, Aditya Bhaskara +5
With the rise in edge-computing devices, there has been an increasing demand to deploy energy and resource-efficient models. A large body of research has been devoted to developing…
Online Linear Optimization with Many Hints
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar +1
We study an online linear optimization (OLO) problem in which the learner is provided access to "hint" vectors in each round prior to making a decision. In this setting, we dev…
Fair clustering via equitable group representations
Mohsen Abbasi, Aditya Bhaskara, Suresh Venkatasubramanian
What does it mean for a clustering to be fair? One popular approach seeks to ensure that each cluster contains groups in (roughly) the same proportion in which they exist in the po…
Online Learning with Imperfect Hints
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar +1
We consider a variant of the classical online linear optimization problem in which at every step, the online player receives a "hint" vector before choosing the action for that rou…