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2020 – today
- 2022
[i34]Zijing Ou, Tingyang Xu, Qinliang Su, Yingzhen Li, Peilin Zhao, Yatao Bian:
Learning Set Functions Under the Optimal Subset Oracle via Equivariant Variational Inference. CoRR abs/2203.01693 (2022)- [i33]Ryutaro Tanno, Melanie F. Pradier, Aditya V. Nori, Yingzhen Li:
Repairing Neural Networks by Leaving the Right Past Behind. CoRR abs/2207.04806 (2022) - [i32]Harrison Zhu, Carles Balsells Rodas, Yingzhen Li:
Markovian Gaussian Process Variational Autoencoders. CoRR abs/2207.05543 (2022) - 2021
- [c25]Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang:
Meta-Learning Divergences for Variational Inference. AISTATS 2021: 4024-4032 - [c24]Yi Zhu, Ehsan Shareghi, Yingzhen Li, Roi Reichart, Anna Korhonen:
Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification. EACL 2021: 894-908 - [c23]Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato:
Sliced Kernelized Stein Discrepancy. ICLR 2021 - [c22]Wenbo Gong, Kaibo Zhang, Yingzhen Li, José Miguel Hernández-Lobato:
Active Slices for Sliced Stein Discrepancy. ICML 2021: 3766-3776 - [c21]Thomas Henn, Yasukazu Sakamoto, Clément Jacquet, Shunsuke Yoshizawa, Masamichi Andou, Stephen Tchen, Ryosuke Saga, Hiroyuki Ishihara, Katsuhiko Shimizu, Yingzhen Li, Ryutaro Tanno:
A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging. MICCAI (3) 2021: 509-518 - [c20]Andrew Gordon Wilson, Pavel Izmailov, Matthew D. Hoffman, Yarin Gal, Yingzhen Li, Melanie F. Pradier, Sharad Vikram, Andrew Y. K. Foong, Sanae Lotfi, Sebastian Farquhar:
Evaluating Approximate Inference in Bayesian Deep Learning. NeurIPS (Competition and Demos) 2021: 113-124 - [c19]Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li:
Sparse Uncertainty Representation in Deep Learning with Inducing Weights. NeurIPS 2021: 6515-6528 - [c18]Victor Prokhorov, Yingzhen Li, Ehsan Shareghi, Nigel Collier:
Learning Sparse Sentence Encoding without Supervision: An Exploration of Sparsity in Variational Autoencoders. RepL4NLP@ACL-IJCNLP 2021: 34-46 - [i31]Yi Zhu, Ehsan Shareghi, Yingzhen Li, Roi Reichart, Anna Korhonen:
Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification. CoRR abs/2101.10717 (2021) - [i30]Wenbo Gong, Kaibo Zhang, Yingzhen Li, José Miguel Hernández-Lobato:
Active Slices for Sliced Stein Discrepancy. CoRR abs/2102.03159 (2021) - [i29]Angus Lamb, Evgeny Saveliev, Yingzhen Li, Sebastian Tschiatschek, Camilla Longden, Simon Woodhead, José Miguel Hernández-Lobato, Richard E. Turner, Pashmina Cameron, Cheng Zhang:
Contextual HyperNetworks for Novel Feature Adaptation. CoRR abs/2104.05860 (2021) - [i28]Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li:
Sparse Uncertainty Representation in Deep Learning with Inducing Weights. CoRR abs/2105.14594 (2021) - [i27]Wenbo Gong, Yingzhen Li:
Interpreting diffusion score matching using normalizing flow. CoRR abs/2107.10072 (2021) - [i26]Thomas Henn, Yasukazu Sakamoto, Clément Jacquet, Shunsuke Yoshizawa, Masamichi Andou, Stephen Tchen, Ryosuke Saga, Hiroyuki Ishihara, Katsuhiko Shimizu, Yingzhen Li, Ryutaro Tanno:
A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging. CoRR abs/2109.12347 (2021) - 2020
- [c17]Cheng Zhang, Kun Zhang, Yingzhen Li:
A Causal View on Robustness of Neural Networks. NeurIPS 2020 - [c16]Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner:
On the Expressiveness of Approximate Inference in Bayesian Neural Networks. NeurIPS 2020 - [i25]Sebastian Lunz, Yingzhen Li, Andrew W. Fitzgibbon, Nate Kushman:
Inverse Graphics GAN: Learning to Generate 3D Shapes from Unstructured 2D Data. CoRR abs/2002.12674 (2020) - [i24]Cheng Zhang, Kun Zhang, Yingzhen Li:
A Causal View on Robustness of Neural Networks. CoRR abs/2005.01095 (2020) - [i23]Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato:
Sliced Kernelized Stein Discrepancy. CoRR abs/2006.16531 (2020) - [i22]Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang:
Meta-Learning for Variational Inference. CoRR abs/2007.02912 (2020) - [i21]Chaochao Lu, Richard E. Turner, Yingzhen Li, Nate Kushman:
Interpreting Spatially Infinite Generative Models. CoRR abs/2007.12411 (2020) - [i20]Victor Prokhorov, Yingzhen Li, Ehsan Shareghi, Nigel Collier:
Hierarchical Sparse Variational Autoencoder for Text Encoding. CoRR abs/2009.12421 (2020) - [i19]Philip J. Ball, Yingzhen Li, Angus Lamb, Cheng Zhang:
A Study on Efficiency in Continual Learning Inspired by Human Learning. CoRR abs/2010.15187 (2020) - [i18]Haiyan Yin, Yingzhen Li, Sinno Jialin Pan, Cheng Zhang, Sebastian Tschiatschek:
Reinforcement Learning with Efficient Active Feature Acquisition. CoRR abs/2011.00825 (2020)
2010 – 2019
- 2019
- [c15]Chao Ma, Sebastian Tschiatschek, Yingzhen Li, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang:
HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals. AABI 2019: 1-8 - [c14]Victor Prokhorov, Ehsan Shareghi, Yingzhen Li, Mohammad Taher Pilehvar, Nigel Collier:
On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation. NGT@EMNLP-IJCNLP 2019: 118-127 - [c13]Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato:
Meta-Learning For Stochastic Gradient MCMC. ICLR (Poster) 2019 - [c12]Yingzhen Li, John Bradshaw, Yash Sharma:
Are Generative Classifiers More Robust to Adversarial Attacks? ICML 2019: 3804-3814 - [c11]Chao Ma, Yingzhen Li, José Miguel Hernández-Lobato:
Variational Implicit Processes. ICML 2019: 4222-4233 - [c10]Ehsan Shareghi, Yingzhen Li, Yi Zhu, Roi Reichart, Anna Korhonen:
Bayesian Learning for Neural Dependency Parsing. NAACL-HLT (1) 2019: 3509-3519 - [c9]Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann:
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. NeurIPS 2019: 13956-13968 - [i17]Anna-Lena Popkes, Hiske Overweg, Ari Ercole
, Yingzhen Li, José Miguel Hernández-Lobato, Yordan Zaykov, Cheng Zhang:
Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care. CoRR abs/1905.02599 (2019) - [i16]Andrew Y. K. Foong, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner:
'In-Between' Uncertainty in Bayesian Neural Networks. CoRR abs/1906.11537 (2019) - [i15]Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner:
Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks. CoRR abs/1909.00719 (2019) - [i14]Victor Prokhorov, Ehsan Shareghi, Yingzhen Li, Mohammad Taher Pilehvar, Nigel Collier:
On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation. CoRR abs/1909.13668 (2019) - [i13]Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann:
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. CoRR abs/1910.12911 (2019) - 2018
- [b1]Yingzhen Li:
Approximate inference: new visions. University of Cambridge, UK, 2018 - [c8]Yingzhen Li, Richard E. Turner:
Gradient Estimators for Implicit Models. ICLR (Poster) 2018 - [c7]Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner:
Variational Continual Learning. ICLR (Poster) 2018 - [c6]Yingzhen Li, Stephan Mandt:
Disentangled Sequential Autoencoder. ICML 2018: 5656-5665 - [i12]Yingzhen Li:
Are Generative Classifiers More Robust to Adversarial Attacks? CoRR abs/1802.06552 (2018) - [i11]Yingzhen Li, Stephan Mandt:
A Deep Generative Model for Disentangled Representations of Sequential Data. CoRR abs/1803.02991 (2018) - [i10]Chao Ma, Yingzhen Li, José Miguel Hernández-Lobato:
Variational Implicit Processes. CoRR abs/1806.02390 (2018) - [i9]Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato:
Meta-Learning for Stochastic Gradient MCMC. CoRR abs/1806.04522 (2018) - 2017
- [c5]Yingzhen Li, Yarin Gal:
Dropout Inference in Bayesian Neural Networks with Alpha-divergences. ICML 2017: 2052-2061 - [i8]Yingzhen Li, Richard E. Turner, Qiang Liu:
Approximate Inference with Amortised MCMC. CoRR abs/1702.08343 (2017) - [i7]Yingzhen Li, Yarin Gal:
Dropout Inference in Bayesian Neural Networks with Alpha-divergences. CoRR abs/1703.02914 (2017) - [i6]Yingzhen Li, Richard E. Turner:
Gradient Estimators for Implicit Models. CoRR abs/1705.07107 (2017) - [i5]Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner:
Variational Continual Learning. CoRR abs/1710.10628 (2017) - 2016
- [c4]Thang D. Bui, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li, Richard E. Turner:
Deep Gaussian Processes for Regression using Approximate Expectation Propagation. ICML 2016: 1472-1481 - [c3]José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Thang D. Bui, Daniel Hernández-Lobato, Richard E. Turner:
Black-Box Alpha Divergence Minimization. ICML 2016: 1511-1520 - [c2]Yingzhen Li, Richard E. Turner:
Rényi Divergence Variational Inference. NIPS 2016: 1073-1081 - [i4]Yingzhen Li, Richard E. Turner:
Variational Inference with Rényi Divergence. CoRR abs/1602.02311 (2016) - [i3]Thang D. Bui, Daniel Hernández-Lobato, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner:
Deep Gaussian Processes for Regression using Approximate Expectation Propagation. CoRR abs/1602.04133 (2016) - 2015
- [c1]Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner:
Stochastic Expectation Propagation. NIPS 2015: 2323-2331 - [i2]Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner:
Stochastic Expectation Propagation. CoRR abs/1506.04132 (2015) - 2012
- [i1]Yingzhen Li, Ye Zhang:
Generating ordered list of Recommended Items: a Hybrid Recommender System of Microblog. CoRR abs/1208.4147 (2012)
Coauthor Index
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