13 citations · 60 across the 31 of their papers we have counts for
6 papers · 1 filter
Min-max Submodular Ranking for Multiple Agents
Qingyun Chen, Sungjin Im, Benjamin Moseley +2
In the submodular ranking (SR) problem, the input consists of a set of submodular functions defined on a ground set of elements. The goal is to order elements for all the functions…
Improved Approximations for Unrelated Machine Scheduling
Sungjin Im, Shi Li
We revisit two well-studied scheduling problems in the unrelated machines setting where each job can have a different processing time on each machine. For minimizing total weighted…
Optimizing Polymatroid Functions
Sungjin Im, Benjamin Moseley, Hung Q. Ngo +2
We consider a class of optimization problems that involve determining the maximum value that a function in a particular class can attain subject to a collection of difference const…
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 ()…
Algorithms with Prediction Portfolios
Michael Dinitz, Sungjin Im, Thomas Lavastida +2
The research area of algorithms with predictions has seen recent success showing how to incorporate machine learning into algorithm design to improve performance when the predictio…
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…