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Interpreting diffusion score matching using normalizing flow


Jul 21, 2021
Wenbo Gong, Yingzhen Li

* 8 pages, International Conference on Machine Learning (ICML) INNF+ 2021 Workshop Spotlight 

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Sparse Uncertainty Representation in Deep Learning with Inducing Weights


May 30, 2021
Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li


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Contextual HyperNetworks for Novel Feature Adaptation


Apr 12, 2021
Angus Lamb, Evgeny Saveliev, Yingzhen Li, Sebastian Tschiatschek, Camilla Longden, Simon Woodhead, José Miguel Hernández-Lobato, Richard E. Turner, Pashmina Cameron, Cheng Zhang

* 17 pages, 9 Figures, workshop paper at NeurIPS 2020 Meta-Learning Workshop 

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Active Slices for Sliced Stein Discrepancy


Feb 08, 2021
Wenbo Gong, Kaibo Zhang, Yingzhen Li, José Miguel Hernández-Lobato

* 22 pages, 7 figures 

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Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification


Jan 26, 2021
Yi Zhu, Ehsan Shareghi, Yingzhen Li, Roi Reichart, Anna Korhonen

* EACL 2021 

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Reinforcement Learning with Efficient Active Feature Acquisition


Nov 02, 2020
Haiyan Yin, Yingzhen Li, Sinno Jialin Pan, Cheng Zhang, Sebastian Tschiatschek


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A Study on Efficiency in Continual Learning Inspired by Human Learning


Oct 28, 2020
Philip J. Ball, Yingzhen Li, Angus Lamb, Cheng Zhang

* Accepted at NeurIPS 2020 BabyMind Workshop 

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Hierarchical Sparse Variational Autoencoder for Text Encoding


Sep 25, 2020
Victor Prokhorov, Yingzhen Li, Ehsan Shareghi, Nigel Collier


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Interpreting Spatially Infinite Generative Models


Jul 24, 2020
Chaochao Lu, Richard E. Turner, Yingzhen Li, Nate Kushman

* ICML 2020 workshop on Human Interpretability in Machine Learning (WHI 2020) 

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Meta-Learning for Variational Inference


Jul 06, 2020
Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang


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Sliced Kernelized Stein Discrepancy


Jun 30, 2020
Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato


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A Causal View on Robustness of Neural Networks


May 03, 2020
Cheng Zhang, Kun Zhang, Yingzhen Li


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Inverse Graphics GAN: Learning to Generate 3D Shapes from Unstructured 2D Data


Feb 28, 2020
Sebastian Lunz, Yingzhen Li, Andrew Fitzgibbon, Nate Kushman

* 8 pages paper, 3 pages references, 18 pages appendix 

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Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck


Oct 28, 2019
Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann

* Published at Neurips 2019 

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On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation


Sep 30, 2019
Victor Prokhorov, Ehsan Shareghi, Yingzhen Li, Mohammad Taher Pilehvar, Nigel Collier

* 10 pages; Accepted in 3rd Workshop on Neural Generation and Translation (WNGT 2019) 

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Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks


Sep 02, 2019
Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner


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'In-Between' Uncertainty in Bayesian Neural Networks


Jun 27, 2019
Andrew Y. K. Foong, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner

* Presented at the ICML 2019 Workshop on Uncertainty and Robustness in Deep Learning 

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Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care


May 07, 2019
Anna-Lena Popkes, Hiske Overweg, Ari Ercole, Yingzhen Li, José Miguel Hernández-Lobato, Yordan Zaykov, Cheng Zhang


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Are Generative Classifiers More Robust to Adversarial Attacks?


Jul 09, 2018
Yingzhen Li, John Bradshaw, Yash Sharma

* Presented as an oral talk at the ICML 2018 workshop on Theoretical Foundations and Applications of Deep Generative Models 

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Disentangled Sequential Autoencoder


Jun 12, 2018
Yingzhen Li, Stephan Mandt


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Meta-Learning for Stochastic Gradient MCMC


Jun 12, 2018
Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato


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Variational Implicit Processes


Jun 06, 2018
Chao Ma, Yingzhen Li, José Miguel Hernández-Lobato


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Variational Continual Learning


May 20, 2018
Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner

* Published at International Conference on Learning Representations (ICLR) 2018 

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Gradient Estimators for Implicit Models


Apr 26, 2018
Yingzhen Li, Richard E. Turner

* v5 fixed a typo in Figure 3 of v4 (the version at ICLR 2018 main conference) 

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Approximate Inference with Amortised MCMC


May 22, 2017
Yingzhen Li, Richard E. Turner, Qiang Liu


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Dropout Inference in Bayesian Neural Networks with Alpha-divergences


Mar 08, 2017
Yingzhen Li, Yarin Gal


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RĂ©nyi Divergence Variational Inference


Oct 28, 2016
Yingzhen Li, Richard E. Turner

* NIPS 2016 

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Black-box $α$-divergence Minimization


Jun 01, 2016
José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Daniel Hernández-Lobato, Thang Bui, Richard E. Turner

* Accepted at ICML 2016. The first version (v1) was presented at NIPS workshops on Advances in Approximate Bayesian Inference and Black Box Learning and Inference 

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Deep Gaussian Processes for Regression using Approximate Expectation Propagation


Feb 12, 2016
Thang D. Bui, Daniel Hernández-Lobato, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner


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Stochastic Expectation Propagation for Large Scale Gaussian Process Classification


Dec 04, 2015
Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li, Thang Bui, Richard E. Turner


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