8 papers
Multiagent Matroid Upgrading: Greedy is Fair and Efficient
Qingwen Ma, Chao Peng, Changfeng Xu +2
This paper introduces a general multiagent matroid upgrading problem that models a broad class of real-world resource allocation tasks. In this setting, there are multiple agents a…
Richer Representations for Neural Algorithmic Reasoning via Auxiliary Reconstruction
Jiafu Huang, Chao Peng, Chenyang Xu +7
Neural algorithmic reasoning has emerged as a popular research direction. It aims to train neural networks to mimic the step-by-step behavior of classical rule-based algorithms. Mo…
Self-Augmented Mixture-of-Experts for QoS Prediction
Kecheng Cai, Chao Peng, Chenyang Xu +4
Quality of Service (QoS) prediction is one of the most fundamental problems in service computing and personalized recommendation. In the problem, there is a set of users and servic…
Combating Spurious Correlations in Graph Interpretability via Self-Reflection
Kecheng Cai, Chenyang Xu, Chao Peng +3
Interpretable graph learning has recently emerged as a popular research topic in machine learning. The goal is to identify the important nodes and edges of an input graph that are…
CuDIP: Enhancing Theorem Proving in LLMs via Curriculum Learning-based Direct Preference Optimization
Shuming Shi, Ruobing Zuo, Gaolei He +3
Automated theorem proving (ATP) is one of the most challenging mathematical reasoning tasks for Large Language Models (LLMs). Most existing LLM-based ATP methods rely on supervised…
Open-Book Neural Algorithmic Reasoning
Hefei Li, Chao Peng, Chenyang Xu +1
Neural algorithmic reasoning is an emerging area of machine learning that focuses on building neural networks capable of solving complex algorithmic tasks. Recent advancements pred…