3 papers
cs.LG2026
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…
cs.LG2026
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…
cs.LG2026
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…