Publications

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

Working papers

Andrew Y. K. Foong*, David R. Burt*, Yingzhen Li and Richard E. Turner. Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks. 2019.

Cheng Zhang and Yingzhen Li. A Causal View on Robustness of Neural Networks. ICML 2019 Workshop on Understanding and Improving Generalization in Deep Learning.

Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin and Katja Hofmann. Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. Accepted at NeurIPS 2019.

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

Ehsan Shareghi, Yingzhen Li, Yi Zhu, Roi Reichart and Anna Korhonen. Bayesian Learning for Neural Dependency Parsing. NAACL-HLT 2019.

Chao Ma, Yingzhen Li and José Miguel Hernández-Lobato. Variational Implicit Processes. International Conference on Machine Learning (ICML), 2019. code

Yingzhen Li, John Bradshaw and Yash Sharma. Are Generative Classifiers More Robust to Adversarial Attacks? International Conference on Machine Learning (ICML), 2019. code

Wenbo Gong*, Yingzhen Li* and José Miguel Hernández-Lobato. Meta-Learning for Stochastic Gradient MCMC. International Conference on Learning Representations (ICLR), 2019.  code cartoon

Yingzhen Li and Stephan Mandt. Disentangled Sequential Autoencoder. International Conference on Machine Learning (ICML), 2018. sprites data architecture 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. 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

Andrew Y.K. Foong, Yingzhen Li, José Miguel Hernández-Lobato and Richard E. Turner. "In-Between" Uncertainty for Bayesian Neural Networks. ICML 2019 workshop on Uncertainty & Robustness in Deep Learning (oral)

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

Approximate Inference: New Visions. PhD in Engineering, University of Cambridge, June 2018.

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