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

**Working papers**

Hippolyt Ritter, Martin Kukla, Cheng Zhang and **Yingzhen Li**. Sparse Uncertainty Representation in Deep Learning with Inducing Weights. 2021

Wenbo Gong, Kaibo Zhang, **Yingzhen Li** and José Miguel Hernández-Lobato. Active Slices for Slided Stein Discrepancy. accepted at ICML 2021

**Refereed conference papers**

Wenbo Gong, **Yingzhen Li** and José Miguel Hernández-Lobato. Sliced Kernelized Stein Discrepancy. International Conference on Learning Representations (ICLR), 2021.

Ruqi Zhang, **Yingzhen Li**, Chris De Sa, Sam Devlin and Cheng Zhang. Meta-Learning for Variational Inference. International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.

Yi Zhu, Ehsan Shareghi, **Yingzhen Li**, Roi Reichart and Anna Korhonen. Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification. European Chapter of the Association for Computational Linguistics (EACL), 2021.

Andrew Y. K. Foong*, David R. Burt*, **Yingzhen Li** and Richard E. Turner. On the Expressiveness of Approximate Inference in Bayesian Neural Networks. Neural Processing Information Systems (NeurIPS), 2020.

Cheng Zhang, Kun Zhang and **Yingzhen Li**. A Causal View on Robustness of Neural Networks. Neural Processing Information Systems (NeurIPS), 2020.

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. Neural Processing Information Systems (NeurIPS), 2019.

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 cartoon

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

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

**Workshop Preprints**

Sebastian Lunz, **Yingzhen Li**, Andrew Fitzgibbon and Nate Kushman. Inverse Graphics GAN: Learning to Generate 3D Shapes from Unstructured 2D Data. NeurIPS 2020 workshop on Differentiable vision, graphics, and physics applied to machine learning (DiffCVGP), 2020.

Chaochao Lu, Richard E. Turner, **Yingzhen Li** and Nate Kushman. Interpreting Spatially Infinite Generative Models. ICML 2020 Workshop on Human Interpretability in Machine Learning (WHI), 2020.

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**. 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. ICML 2017 Workshop on Implicit Generative Models. 2017. cartoon

**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]