463 citations · 5.1k across the 190 of their papers we have counts for
56 papers · 1 filter
Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations
Paidamoyo Chapfuwa, Sherri Rose, Lawrence Carin +2
End-to-end learning of dynamical systems with black-box models, such as neural ordinary differential equations (ODEs), provides a flexible framework for learning dynamics from data…
Simpler, Faster, Stronger: Breaking The log-K Curse On Contrastive Learners With FlatNCE
Junya Chen, Zhe Gan, Xuan Li +10
InfoNCE-based contrastive representation learners, such as SimCLR, have been tremendously successful in recent years. However, these contrastive schemes are notoriously resource de…
Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimization
Qing Guo, Junya Chen, Dong Wang +6
Successful applications of InfoNCE and its variants have popularized the use of contrastive variational mutual information (MI) estimators in machine learning. While featuring supe…
Double Robust Representation Learning for Counterfactual Prediction
Shuxi Zeng, Serge Assaad, Chenyang Tao +3
Causal inference, or counterfactual prediction, is central to decision making in healthcare, policy and social sciences. To de-bias causal estimators with high-dimensional data in…
Counterfactual Representation Learning with Balancing Weights
Serge Assaad, Shuxi Zeng, Chenyang Tao +5
A key to causal inference with observational data is achieving balance in predictive features associated with each treatment type. Recent literature has explored representation lea…
GO Hessian for Expectation-Based Objectives
Yulai Cong, Miaoyun Zhao, Jianqiao Li +2
An unbiased low-variance gradient estimator, termed GO gradient, was proposed recently for expectation-based objectives $\mathbb{E}_{q_{\boldsymbolγ}(\boldsymbol{y})} [f(\boldsymbo…
Enabling Counterfactual Survival Analysis with Balanced Representations
Paidamoyo Chapfuwa, Serge Assaad, Shuxi Zeng +3
Balanced representation learning methods have been applied successfully to counterfactual inference from observational data. However, approaches that account for survival outcomes…
Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization
Shijing Si, Chris. J. Oates, Andrew B. Duncan +2
Control variates are a well-established tool to reduce the variance of Monte Carlo estimators. However, for large-scale problems including high-dimensional and large-sample setting…
Survival Cluster Analysis
Paidamoyo Chapfuwa, Chunyuan Li, Nikhil Mehta +2
Conventional survival analysis approaches estimate risk scores or individualized time-to-event distributions conditioned on covariates. In practice, there is often great population…
Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods
Kevin J Liang, Guoyin Wang, Yitong Li +2
We investigate time-dependent data analysis from the perspective of recurrent kernel machines, from which models with hidden units and gated memory cells arise naturally. By consid…
Adversarial Self-Paced Learning for Mixture Models of Hawkes Processes
Dixin Luo, Hongteng Xu, Lawrence Carin
We propose a novel adversarial learning strategy for mixture models of Hawkes processes, leveraging data augmentation techniques of Hawkes process in the framework of self-paced le…
Syntax-Infused Variational Autoencoder for Text Generation
Xinyuan Zhang, Yi Yang, Siyang Yuan +2
We present a syntax-infused variational autoencoder (SIVAE), that integrates sentences with their syntactic trees to improve the grammar of generated sentences. Distinct from exist…
Survival Function Matching for Calibrated Time-to-Event Predictions
Paidamoyo Chapfuwa, Chenyang Tao, Lawrence Carin +1
Models for predicting the time of a future event are crucial for risk assessment, across a diverse range of applications. Existing time-to-event (survival) models have focused prim…
Scalable Thompson Sampling via Optimal Transport
Ruiyi Zhang, Zheng Wen, Changyou Chen +1
Thompson sampling (TS) is a class of algorithms for sequential decision-making, which requires maintaining a posterior distribution over a model. However, calculating exact posteri…
GO Gradient for Expectation-Based Objectives
Yulai Cong, Miaoyun Zhao, Ke Bai +1
Within many machine learning algorithms, a fundamental problem concerns efficient calculation of an unbiased gradient wrt parameters $\gammav$ for expectation-based objectives $\Eb…
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…
On Connecting Stochastic Gradient MCMC and Differential Privacy
Bai Li, Changyou Chen, Hao Liu +1
Significant success has been realized recently on applying machine learning to real-world applications. There have also been corresponding concerns on the privacy of training data,…
Symmetric Variational Autoencoder and Connections to Adversarial Learning
Liqun Chen, Shuyang Dai, Yunchen Pu +3
A new form of the variational autoencoder (VAE) is proposed, based on the symmetric Kullback-Leibler divergence. It is demonstrated that learning of the resulting symmetric VAE (sV…