1 citations · 1 across the 3 of their papers we have counts for
4 papers
Compressing Value Predictions for Learning-Augmented Metrical Task Systems
Sizhe Li, Yecheng Li, Kun He
Learning-augmented algorithms for metrical task systems (MTS) can exploit predictions of canonical dual values, but existing formulations typically require a prediction for every s…
Learning to Rewrite Prompts for Bootstrapping LLMs on Downstream Tasks
Qinhao Zhou, Xiang Xiang, Kun He +1
In recent years, the growing interest in Large Language Models (LLMs) has significantly advanced prompt engineering, transitioning from manual design to model-based optimization. P…
Phase transition of the Sinkhorn-Knopp algorithm
Kun He
The matrix scaling problem, particularly the Sinkhorn-Knopp algorithm, has been studied for over 60 years. In practice, the algorithm often yields high-quality approximations withi…
LocalEscaper: A Weakly-supervised Framework with Regional Reconstruction for Scalable Neural TSP Solvers
Junrui Wen, Yifei Li, Bart Selman +1
Neural solvers have shown significant potential in solving the Traveling Salesman Problem (TSP), yet current approaches face significant challenges. Supervised learning (SL)-based…