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papers

Publications (16)

Approximate Inference with Amortised MCMC

Yingzhen Li, Richard E. Turner, Qiang Liu

Dropout Inference in Bayesian Neural Networks with Alpha-divergences

Yingzhen Li, Yarin Gal

Are Generative Classifiers More Robust to Adversarial Attacks?

Yingzhen Li, John Bradshaw, Yash Sharma

Stochastic Expectation Propagation for Large Scale Gaussian Process Classification

Daniel HernÃ¡ndez-Lobato, JosÃ© Miguel HernÃ¡ndez-Lobato, Yingzhen Li +2

Disentangled Sequential Autoencoder

Yingzhen Li, Stephan Mandt

Gradient Estimators for Implicit Models

Yingzhen Li, Richard E. Turner

Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation

Thang D. Bui, JosÃ© Miguel HernÃ¡ndez-Lobato, Yingzhen Li +2

Variational Implicit Processes

Chao Ma, Yingzhen Li, JosÃ© Miguel HernÃ¡ndez-Lobato

Generating ordered list of Recommended Items: a Hybrid Recommender System of Microblog

Yingzhen Li, Ye Zhang

RÃ©nyi Divergence Variational Inference

Yingzhen Li, Richard E. Turner

Black-box $Î±$-divergence Minimization

JosÃ© Miguel HernÃ¡ndez-Lobato, Yingzhen Li, Mark Rowland +3

'In-Between' Uncertainty in Bayesian Neural Networks

Andrew Y. K. Foong, Yingzhen Li, JosÃ© Miguel HernÃ¡ndez-Lobato +1

Stochastic Expectation Propagation

Yingzhen Li, Jose Miguel Hernandez-Lobato, Richard E. Turner

Meta-Learning for Stochastic Gradient MCMC

Wenbo Gong, Yingzhen Li, JosÃ© Miguel HernÃ¡ndez-Lobato

Deep Gaussian Processes for Regression using Approximate Expectation Propagation

Thang D. Bui, Daniel HernÃ¡ndez-Lobato, Yingzhen Li +2

Variational Continual Learning

Cuong V. Nguyen, Yingzhen Li, Thang D. Bui +1