50 citations · 73 across the 10 of their papers we have counts for
10 papers
Uncertainty-Aware Robust Learning on Noisy Graphs
Shuyi Chen, Kaize Ding, Shixiang Zhu
Graph neural networks (GNNs) have excelled in various graph learning tasks, particularly node classification. However, their performance is often hampered by noisy measurements in…
Counterfactual Generative Models for Time-Varying Treatments
Shenghao Wu, Wenbin Zhou, Minshuo Chen +1
Estimating the counterfactual outcome of treatment is essential for decision-making in public health and clinical science, among others. Often, treatments are administered in a seq…
Conditional Generative Modeling for High-dimensional Marked Temporal Point Processes
Zheng Dong, Zekai Fan, Shixiang Zhu
Point processes offer a versatile framework for sequential event modeling. However, the computational challenges and constrained representational power of the existing point proces…
Balanced Districting on Grid Graphs with Provable Compactness and Contiguity
Cyrus Hettle, Shixiang Zhu, Swati Gupta +1
Given a graph with vertex weights and a desired number of parts , the goal in graph partitioning problems is to partition the vertex set V into parts $V_1,\ld…
High-resolution Spatio-temporal Model for County-level COVID-19 Activity in the U.S
Shixiang Zhu, Alexander Bukharin, Liyan Xie +3
We present an interpretable high-resolution spatio-temporal model to estimate COVID-19 deaths together with confirmed cases one-week ahead of the current time, at the county-level…
Goodness-of-Fit Test for Mismatched Self-Exciting Processes
Song Wei, Shixiang Zhu, Minghe Zhang +1
Recently there have been many research efforts in developing generative models for self-exciting point processes, partly due to their broad applicability for real-world application…
Imitation Learning of Neural Spatio-Temporal Point Processes
Shixiang Zhu, Shuang Li, Zhigang Peng +1
We present a novel Neural Embedding Spatio-Temporal (NEST) point process model for spatio-temporal discrete event data and develop an efficient imitation learning (a type of reinfo…
Learning Temporal Point Processes via Reinforcement Learning
Shuang Li, Shuai Xiao, Shixiang Zhu +3
Social goods, such as healthcare, smart city, and information networks, often produce ordered event data in continuous time. The generative processes of these event data can be ver…
Crime Event Embedding with Unsupervised Feature Selection
Shixiang Zhu, Yao Xie
We present a novel event embedding algorithm for crime data that can jointly capture time, location, and the complex free-text component of each event. The embedding is achieved by…
Crime incidents embedding using restricted Boltzmann machines
Shixiang Zhu, Yao Xie
We present a new approach for detecting related crime series, by unsupervised learning of the latent feature embeddings from narratives of crime record via the Gaussian-Bernoulli R…