98 citations · 116 across the 9 of their papers we have counts for
9 papers
A Survey of Weight Space Learning: Understanding, Representation, and Generation
Xiaolong Han, Zehong Wang, Bo Zhao +8
Neural network weights are typically viewed as the end product of training, while most deep learning research focuses on data, features, and architectures. However, recent advances…
Generalizing GNNs with Tokenized Mixture of Experts
Xiaoguang Guo, Zehong Wang, Jiazheng Li +5
Deployed graph neural networks (GNNs) are frozen at deployment yet must fit clean data, generalize under distribution shifts, and remain stable to perturbations. We show that stati…
Energy-Based Models for Predicting Mutational Effects on Proteins
Patrick Soga, Zhenyu Lei, Yinhan He +2
Predicting changes in binding free energy () is a vital task in protein engineering and protein-protein interaction (PPI) engineering for drug discovery. Previous works have o…
Collaborative Large Language Model for Recommender Systems
Yaochen Zhu, Liang Wu, Qi Guo +2
Recently, there has been growing interest in developing the next-generation recommender systems (RSs) based on pretrained large language models (LLMs). However, the semantic gap be…
GiGaMAE: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstruction
Yucheng Shi, Yushun Dong, Qiaoyu Tan +2
Self-supervised learning with masked autoencoders has recently gained popularity for its ability to produce effective image or textual representations, which can be applied to vari…
Path-Specific Counterfactual Fairness for Recommender Systems
Yaochen Zhu, Jing Ma, Liang Wu +3
Recommender systems (RSs) have become an indispensable part of online platforms. With the growing concerns of algorithmic fairness, RSs are not only expected to deliver high-qualit…
When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?
Yushun Dong, Jundong Li, Tobias Schnabel
In recent years, neural models have been repeatedly touted to exhibit state-of-the-art performance in recommendation. Nevertheless, multiple recent studies have revealed that the r…
Fairness in Graph Mining: A Survey
Yushun Dong, Jing Ma, Song Wang +2
Graph mining algorithms have been playing a significant role in myriad fields over the years. However, despite their promising performance on various graph analytical tasks, most o…
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective
Jihong Wang, Minnan Luo, Jundong Li +3
Recent studies have revealed that GNNs are vulnerable to adversarial attacks. Most existing robust graph learning methods measure model robustness based on label information, rende…