463 citations · 5.1k across the 190 of their papers we have counts for
12 papers · 2 filters
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…
Benefits from Superposed Hawkes Processes
Hongteng Xu, Dixin Luo, Xu Chen +1
The superposition of temporal point processes has been studied for many years, although the usefulness of such models for practical applications has not be fully developed. We inve…
Learning Registered Point Processes from Idiosyncratic Observations
Hongteng Xu, Lawrence Carin, Hongyuan Zha
A parametric point process model is developed, with modeling based on the assumption that sequential observations often share latent phenomena, while also possessing idiosyncratic…
A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks
Qinliang Su, Xuejun Liao, Lawrence Carin
We present a probabilistic framework for nonlinearities, based on doubly truncated Gaussian distributions. By setting the truncation points appropriately, we are able to generate v…
A Convergence Analysis for A Class of Practical Variance-Reduction Stochastic Gradient MCMC
Changyou Chen, Wenlin Wang, Yizhe Zhang +2
Stochastic gradient Markov Chain Monte Carlo (SG-MCMC) has been developed as a flexible family of scalable Bayesian sampling algorithms. However, there has been little theoretical…
ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching
Chunyuan Li, Hao Liu, Changyou Chen +4
We investigate the non-identifiability issues associated with bidirectional adversarial training for joint distribution matching. Within a framework of conditional entropy, we prop…
Continuous-Time Flows for Efficient Inference and Density Estimation
Changyou Chen, Chunyuan Li, Liqun Chen +3
Two fundamental problems in unsupervised learning are efficient inference for latent-variable models and robust density estimation based on large amounts of unlabeled data. Algorit…
Deep Generative Models for Relational Data with Side Information
Changwei Hu, Piyush Rai, Lawrence Carin
We present a probabilistic framework for overlapping community discovery and link prediction for relational data, given as a graph. The proposed framework has: (1) a deep architect…
Adversarial Feature Matching for Text Generation
Yizhe Zhang, Zhe Gan, Kai Fan +4
The Generative Adversarial Network (GAN) has achieved great success in generating realistic (real-valued) synthetic data. However, convergence issues and difficulties dealing with…
Stochastic Gradient Monomial Gamma Sampler
Yizhe Zhang, Changyou Chen, Zhe Gan +2
Recent advances in stochastic gradient techniques have made it possible to estimate posterior distributions from large datasets via Markov Chain Monte Carlo (MCMC). However, when t…
Compressive Sensing via Convolutional Factor Analysis
Xin Yuan, Yunchen Pu, Lawrence Carin
We solve the compressive sensing problem via convolutional factor analysis, where the convolutional dictionaries are learned {\em in situ} from the compressed measurements. An alte…