6 citations · 7 across the 5 of their papers we have counts for
5 papers · 1 filter
Mixture-of-Experts Operator Transformer for Large-Scale PDE Pre-Training
Hong Wang, Haiyang Xin, Jie Wang +4
Pre-training has proven effective in addressing data scarcity and performance limitations in solving PDE problems with neural operators. However, challenges remain due to the heter…
CausalPre: Scalable and Effective Data Pre-Processing for Causal Fairness
Ying Zheng, Yangfan Jiang, Kian-Lee Tan
Causal fairness in databases is crucial to preventing biased and inaccurate outcomes in downstream tasks. While most prior work assumes a known causal model, recent efforts relax t…
Predictive Response Optimization: Using Reinforcement Learning to Fight Online Social Network Abuse
Garrett Wilson, Geoffrey Goh, Yan Jiang +4
Detecting phishing, spam, fake accounts, data scraping, and other malicious activity in online social networks (OSNs) is a problem that has been studied for well over a decade, wit…
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning
Ruben Ohana, Michael McCabe, Lucas Meyer +24
Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small…
On the Robustness of Post-hoc GNN Explainers to Label Noise
Zhiqiang Zhong, Yangqianzi Jiang, Davide Mottin
Proposed as a solution to the inherent black-box limitations of graph neural networks (GNNs), post-hoc GNN explainers aim to provide precise and insightful explanations of the beha…