From the 1 of 9 linked papers with an AI index.
9 papers
Approximating (Weighted) Chromatic Correlation Clustering via Cluster LP
Fateme Abbasi, Hyung-Chan An, JarosÅaw Byrka +2
The paper presents a (2+ε)-approximation algorithm for Chromatic Correlation Clustering and its weighted variant by extending the cluster linear programming formulation to handle c…
Parsimonious Learning-Augmented Online Metric Matching
Yongho Shin, Phanu Vajanopath
Learning-augmented algorithms have received significant attention in recent years, particularly in the context of online optimization. Motivated by the high computational cost of g…
Optimal Learning-Augmented Algorithm for Online Bidding
Changyeol Lee, Dahoon Lee, Jongseo Lee +2
Recent advances in machine learning have spurred significant interest in learning-augmented algorithms, particularly for online optimization. A growing body of work has studied onl…
Servicing Matched Client Pairs with Facilities
Fateme Abbasi, Martin Böhm, JarosÅaw Byrka +2
We study Facility Location with Matching, a Facility Location problem where, given additional information about which pair of clients is compatible to be matched, we need to match…
Post-Training and Test-Time Scaling of Generative Agent Behavior Models for Interactive Autonomous Driving
Hyunki Seong, Jeong-Kyun Lee, Heesoo Myeong +5
Learning interactive motion behaviors among multiple agents is a core challenge in autonomous driving. While imitation learning models generate realistic trajectories, they often i…
Learning-Augmented Online Bipartite Fractional Matching
Davin Choo, Billy Jin, Yongho Shin
Online bipartite matching is a fundamental problem in online optimization, extensively studied both in its integral and fractional forms due to its theoretical significance and pra…