most citedImproving Online Algorithms via ML Predictions

124 citations · 124 across the 5 of their papers we have counts for

collaborators

6 papers

cs.DS2026

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…

cs.DS2026

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…

cs.DS2026

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…

cs.LG2026

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…

cs.DS2024124 cited

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…

cs.DS2024

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…