463 citations · 5.1k across the 190 of their papers we have counts for
7 papers · 2 filters
Generative Adversarial Network Training is a Continual Learning Problem
Kevin J Liang, Chunyuan Li, Guoyin Wang +1
Generative Adversarial Networks (GANs) have proven to be a powerful framework for learning to draw samples from complex distributions. However, GANs are also notoriously difficult…
Predicting Smoking Events with a Time-Varying Semi-Parametric Hawkes Process Model
Matthew Engelhard, Hongteng Xu, Lawrence Carin +3
Health risks from cigarette smoking -- the leading cause of preventable death in the United States -- can be substantially reduced by quitting. Although most smokers are motivated…
Understanding and Accelerating Particle-Based Variational Inference
Chang Liu, Jingwei Zhuo, Pengyu Cheng +3
Particle-based variational inference methods (ParVIs) have gained attention in the Bayesian inference literature, for their capacity to yield flexible and accurate approximations.…
Adversarial Time-to-Event Modeling
Paidamoyo Chapfuwa, Chenyang Tao, Chunyuan Li +4
Modern health data science applications leverage abundant molecular and electronic health data, providing opportunities for machine learning to build statistical models to support…
Superposition-Assisted Stochastic Optimization for Hawkes Processes
Hongteng Xu, Xu Chen, Lawrence Carin
We consider the learning of multi-agent Hawkes processes, a model containing multiple Hawkes processes with shared endogenous impact functions and different exogenous intensities.…
Multi-Label Learning from Medical Plain Text with Convolutional Residual Models
Xinyuan Zhang, Ricardo Henao, Zhe Gan +2
Predicting diagnoses from Electronic Health Records (EHRs) is an important medical application of multi-label learning. We propose a convolutional residual model for multi-label cl…
Learning Structural Weight Uncertainty for Sequential Decision-Making
Ruiyi Zhang, Chunyuan Li, Changyou Chen +1
Learning probability distributions on the weights of neural networks (NNs) has recently proven beneficial in many applications. Bayesian methods, such as Stein variational gradient…