Young-San Lin — accepted AI-conference papers

2025

Learning-Augmented Algorithms for Online Concave Packing and Convex Covering Problems

Elena Grigorescu, Young-San Lin, Maoyuan Song

AISTATS 2025poster

*Learning-augmented algorithms* have been extensively studied in the computer science community recently, particularly in the context of online problems, in which machine-learning predictors can help provide additional information about the future, in order to overcome classical impossibility result…

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2022

Learning-Augmented Algorithms for Online Linear and Semidefinite Programming

Elena Grigorescu, Young-San Lin, Sandeep Silwal, Maoyuan Song, Samson Zhou

NeurIPS 2022accept

Semidefinite programming (SDP) is a unifying framework that generalizes both linear programming and quadratically-constrained quadratic programming, while also yielding efficient solvers, both in theory and in practice. However, there exist known impossibility results for approximating the optimal…

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