124 citations · 124 across the 7 of their papers we have counts for
8 papers
A General Framework for Matching with Delays
Sara Ahmadian, Shuchi Chawla, Ravi Kumar +2
We present a simple deterministic -competitive algorithm for the online minimum-cost perfect matching with delays (MPMD) problem on arbitrary, unknown metric spaces, whe…
Online Algorithms with a Sample: Tight Bounds and Adversarial Robustness
Anish Hebbar, Ravi Kumar, Roie Levin +3
Suppose an online algorithm is given an unbiased -sample of its input as offline advice; can the algorithm exploit the sample to achieve beyond-worst-case performance? We study…
Multi-Level Aggregation via Dual Fitting: An -Competitive Algorithm
Sara Ahmadian, Shuchi Chawla, Ravi Kumar +2
We present a new online algorithm for the well-known Multi-Level Aggregation Problem (MLAP) with arbitrary delay functions, achieving a -competitive ratio, where is the dep…
Stochastic Caching via Subset Entropy
Ravi Kumar, Roie Levin, Joseph +2
A classic approach to beyond worst-case algorithm design is to impose stochastic assumptions on the input. However, a limiting feature of stochastic analyses is that, by the min-ma…
Sketching Intersection Profiles: A Simple Proof and Three Applications
Flavio Chierichetti, Mirko Giacchini, Ravi Kumar +3
In this work we settle the complexity of three sketching problems. (i) We show that sketching vertex neighborhood sizes in graphs requires bits, standing in sharp contrast…
Adaptive Weighted Averaging
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar +1
We study the problem of selecting the largest among unknown values given only a single unbiased estimate for each . We design strategies that are sim…
Improving Online Algorithms via ML Predictions
Ravi Kumar, Manish Purohit, Zoya Svitkina
In this work we study the problem of using machine-learned predictions to improve the performance of online algorithms. We consider two classical problems, ski rental and non-clair…
Online Load and Graph Balancing for Random Order Inputs
Sungjin Im, Ravi Kumar, Shi Li +2
Online load balancing for heterogeneous machines aims to minimize the makespan (maximum machine workload) by scheduling arriving jobs with varying sizes on different machines. In t…