22 citations · 38 across the 14 of their papers we have counts for
5 papers · 1 filter
Upper Confidence Bounds for Combining Stochastic Bandits
Ashok Cutkosky, Abhimanyu Das, Manish Purohit
We provide a simple method to combine stochastic bandit algorithms. Our approach is based on a "meta-UCB" procedure that treats each of individual bandit algorithms as arms in…
Learning-Augmented Weighted Paging
Nikhil Bansal, Christian Coester, Ravi Kumar +2
We consider a natural semi-online model for weighted paging, where at any time the algorithm is given predictions, possibly with errors, about the next arrival of each page. The mo…
Strategy-proof and Envy-free Mechanisms for House Allocation
Priyanka Shende, Manish Purohit
We consider the problem of allocating indivisible objects to agents when agents have strict preferences over objects. There are inherent trade-offs between competing notions of eff…
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…
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…