151 citations · 632 across the 84 of their papers we have counts for
17 papers · 1 filter
Class-conditioned Domain Generalization via Wasserstein Distributional Robust Optimization
Jingge Wang, Yang Li, Liyan Xie +1
Given multiple source domains, domain generalization aims at learning a universal model that performs well on any unseen but related target domain. In this work, we focus on the do…
Sinkhorn Distributionally Robust Optimization
Jie Wang, Rui Gao, Yao Xie
We study distributionally robust optimization with Sinkhorn distance -- a variant of Wasserstein distance based on entropic regularization. We derive a convex programming dual refo…
Conformal Anomaly Detection on Spatio-Temporal Observations with Missing Data
Chen Xu, Yao Xie
We develop a distribution-free, unsupervised anomaly detection method called ECAD, which wraps around any regression algorithm and sequentially detects anomalies. Rooted in conform…
Neural Spectral Marked Point Processes
Shixiang Zhu, Haoyun Wang, Zheng Dong +2
Self- and mutually-exciting point processes are popular models in machine learning and statistics for dependent discrete event data. To date, most existing models assume stationary…
Neural Tangent Kernel Maximum Mean Discrepancy
Xiuyuan Cheng, Yao Xie
We present a novel neural network Maximum Mean Discrepancy (MMD) statistic by identifying a new connection between neural tangent kernel (NTK) and MMD. This connection enables us t…
Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data
Shixiang Zhu, Alexander Bukharin, Liyan Xie +4
Recently, the Centers for Disease Control and Prevention (CDC) has worked with other federal agencies to identify counties with increasing coronavirus disease 2019 (COVID-19) incid…