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

**Working papers**

**Yingzhen Li** and Richard E. Turner. Gradient Estimators for Implicit Models. Submitted to NIPS 2017. cartoon

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

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

**Refereed conference papers**

**Yingzhen Li** and Yarin Gal. Dropout Inference in Bayesian Neural Networks with Alpha-divergences. accepted at ICML 2017. 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

José Miguel Hernández-Lobato*, **Yingzhen Li***, Mark Rowland, Daniel Hernández-Lobato, Thang Bui and Richard E. Turner. Black-box α-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

**Preprints**

**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

**Talks**

Approximate inference with amortised MCMC. CamAIML workshop, Mar 2017

Objective functions for variational auto-encoders. @ Twitter Cortex Vx (previously Magic Pony), Sept 2016

Variational inference with Rényi divergence. MSR-MLG joint workshop, Mar 2016

Alpha divergence back to conversation. Research Talk @ CBL, Feb 2016

Stochastic optimisation and adaptive learning rates. Reading Group Talk @ CBL (with Mark Rowland), Nov 2015 [code]

Concave-convex procedure. Tea Talk @ CBL, Oct 2015

*Expectation propagation as a way of life.* Tea Talk @ CBL, Feb 2015. [reference]

*Introduction to transfer learning.* Reading Group Talk @ CBL (with Pei-Hao Su), Jan 2015.

*Bayesian learning for restricted Boltzmann machines.* Research Talk @ CBL, Nov 2014.

*On Restricted Boltzmann Machine Learning.* Guest Talk @ Dept of Math, SYSU, Jun 2014.

*Loopy Belief Propagation.* RCC Talk @ CBL (with Alex Matthews), Apr 2014.

*The Importance of Encoding.* Tea Talk @ CBL, Feb 2014. [reference]

I*T & ML: Channels, Quantizers, and Divergences.* RCC Talk @ CBL (with Antonio Artés-Rodríguez), Jan 2014.

**Thesis**

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