22 citations · 34 across the 7 of their papers we have counts for
13 papers
Parsimonious Learning-Augmented Caching
Sungjin Im, Ravi Kumar, Aditya Petety +1
Learning-augmented algorithms -- in which, traditional algorithms are augmented with machine-learned predictions -- have emerged as a framework to go beyond worst-case analysis. Th…
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…
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…