Publications

(To save your time you are encouraged to look at the cartoon illustrations for a taste ūüėÜ)

Working papers

Wenbo Gong*, Yingzhen Li* and José Miguel Hernández-Lobato. Meta-Learning for Stochastic Gradient MCMC. 2018

Chao Ma, Yingzhen Li and José Miguel Hernández-Lobato. Variational Implicit Processes. 2018

Yingzhen Li. Are Generative Classifiers More Robust to Adversarial Attacks?.  2018.

Yingzhen Li. Approximate Gradient Descent for Training Implicit Generative Models. NIPS 2017 Bayesian Deep Learning workshop. 2017

Yingzhen Li, Richard E. Turner and Qiang Liu. Approximate Inference with Amortised MCMC. 2017. cartoon

Refereed conference papers

Yingzhen Li and Stephan Mandt. Disentangled Sequential Autoencoder. Accepted at ICML 2018. sprites data  cartoon

Cuong V. Nguyen, Yingzhen Li, Thang D. Bui and Richard E. Turner. Variational Continual Learning. International Conference on Learning Representations (ICLR), 2018.  code cartoon

Yingzhen Li and Richard E. Turner. Gradient Estimators for Implicit Models. International Conference on Learning Representations (ICLR), 2018.  code cartoon

Yingzhen Li and Yarin Gal. Dropout Inference in Bayesian Neural Networks with Alpha-divergences. International Conference on Machine Learning (ICML), 2017.  code cartoon

Yingzhen Li and Richard E. Turner. Rényi Divergence Variational Inference. Neural Processing Information Systems (NIPS), 2016.    (Previously titled "Variational Inference with Rényi Divergence")  code  cartoon

Jos√© Miguel Hern√°ndez-Lobato*, Yingzhen Li*, Mark Rowland, Daniel Hern√°ndez-Lobato, Thang Bui¬†and¬†Richard E. Turner. Black-box őĪ-divergence Minimization. International Conference on Machine Learning (ICML), 2016.¬† code cartoon

Thang Bui, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li and Richard E. Turner. Deep Gaussian Processes for Regression using Approximate Expectation Propagation. International Conference on Machine Learning (ICML), 2016.  code cartoon

Yingzhen Li, Jose Miguel Hernandez-Lobato and Richard E. Turner. Stochastic Expectation Propagation. Neural Processing Information Systems (NIPS), 2015 (spotlight, 4.5%).  demo cartoon

 

Workshop Preprints

Yingzhen Li and Qiang Liu. Wild Variational Approximations. preprint presented in NIPS Advances in approximate inference, 2016. cartoon

Yingzhen Li and Richard E. Turner. A Unifying Approximate Inference Framework from Variational Free Energy Relaxation. NIPS Advances in approximate inference workshop, 2016

Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li, Thang Bui and Richard E. Turner. Stochastic Expectation Propagation for Large Scale Gaussian Process Classification. NIPS Advances in approximate inference workshop (contributed talk), 2015

Yingzhen Li and Ye Zhang. Generating ordered list of Recommended Items: a Hybrid Recommender System of Microblog. 2012

 

 

 

Thesis

Compressed Sensing and Related Learning Problems. B.S. in Mathematics, Sun Yat-sen University, May 2013. (Best B.S. Thesis Award). [slides]

 

Things I've done in my undergrad years