Generating ordered list of Recommended Items: a Hybrid Recommender System of Microblog - Yingzhen Li; Ye Zhang (CoRR, 2012)

Stochastic Expectation Propagation. - Yingzhen Li; José Miguel Hernández-Lobato; Richard E. Turner (NIPS, 2015)

Deep Gaussian Processes for Regression using Approximate Expectation Propagation. - Thang D. Bui; Daniel Hernández-Lobato; José Miguel Hernández-Lobato; Yingzhen Li; Richard E. Turner (ICML, 2016)

Black-Box Alpha Divergence Minimization. - José Miguel Hernández-Lobato; Yingzhen Li; Mark Rowland 001; Thang D. Bui; Daniel Hernández-Lobato; Richard E. Turner (ICML, 2016)

Rényi Divergence Variational Inference. - Yingzhen Li; Richard E. Turner (NIPS, 2016)

Variational Inference with Rényi Divergence. - Yingzhen Li; Richard E. Turner (CoRR, 2016)

Dropout Inference in Bayesian Neural Networks with Alpha-divergences. - Yingzhen Li; Yarin Gal (ICML, 2017)

Approximate Inference with Amortised MCMC. - Yingzhen Li; Richard E. Turner; Qiang Liu (CoRR, 2017)

Gradient Estimators for Implicit Models. - Yingzhen Li; Richard E. Turner (ICLR (Poster), 2018)

Variational Continual Learning. - Cuong V. Nguyen; Yingzhen Li; Thang D. Bui; Richard E. Turner (ICLR (Poster), 2018)

Disentangled Sequential Autoencoder. - Yingzhen Li; Stephan Mandt (ICML, 2018)

Are Generative Classifiers More Robust to Adversarial Attacks? - Yingzhen Li (CoRR, 2018)

A Deep Generative Model for Disentangled Representations of Sequential Data. - Yingzhen Li; Stephan Mandt (CoRR, 2018)

Approximate inference: new visions. - Yingzhen Li (2018)

HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals. - Chao Ma 019; Sebastian Tschiatschek; Yingzhen Li; Richard E. Turner; José Miguel Hernández-Lobato; Cheng Zhang 005 (AABI, 2019)

On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation. - Victor Prokhorov; Ehsan Shareghi; Yingzhen Li; Mohammad Taher Pilehvar; Nigel Collier (NGT@EMNLP-IJCNLP, 2019)

Meta-Learning For Stochastic Gradient MCMC. - Wenbo Gong 001; Yingzhen Li; José Miguel Hernández-Lobato (ICLR (Poster), 2019)

Are Generative Classifiers More Robust to Adversarial Attacks? - Yingzhen Li; John Bradshaw; Yash Sharma 001 (ICML, 2019)

Variational Implicit Processes. - Chao Ma 019; Yingzhen Li; José Miguel Hernández-Lobato (ICML, 2019)

Bayesian Learning for Neural Dependency Parsing. - Ehsan Shareghi; Yingzhen Li; Yi Zhu; Roi Reichart; Anna Korhonen (NAACL-HLT (1), 2019)

Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. - Maximilian Igl; Kamil Ciosek; Yingzhen Li; Sebastian Tschiatschek; Cheng Zhang 005; Sam Devlin; Katja Hofmann (NeurIPS, 2019)

Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care. - Anna-Lena Popkes; Hiske Overweg; Ari Ercole; Yingzhen Li; José Miguel Hernández-Lobato; Yordan Zaykov; Cheng Zhang 005 (CoRR, 2019)

'In-Between' Uncertainty in Bayesian Neural Networks. - Andrew Y. K. Foong; Yingzhen Li; José Miguel Hernández-Lobato; Richard E. Turner (CoRR, 2019)

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

A Causal View on Robustness of Neural Networks. - Cheng Zhang 005; Kun Zhang 001; Yingzhen Li (NeurIPS, 2020)

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

Inverse Graphics GAN: Learning to Generate 3D Shapes from Unstructured 2D Data. - Sebastian Lunz; Yingzhen Li; Andrew W. Fitzgibbon; Nate Kushman (CoRR, 2020)

Meta-Learning for Variational Inference. - Ruqi Zhang; Yingzhen Li; Christopher De Sa; Sam Devlin; Cheng Zhang 005 (CoRR, 2020)

Interpreting Spatially Infinite Generative Models. - Chaochao Lu; Richard E. Turner; Yingzhen Li; Nate Kushman (CoRR, 2020)

Hierarchical Sparse Variational Autoencoder for Text Encoding. - Victor Prokhorov; Yingzhen Li; Ehsan Shareghi; Nigel Collier (CoRR, 2020)

A Study on Efficiency in Continual Learning Inspired by Human Learning. - Philip J. Ball; Yingzhen Li; Angus Lamb; Cheng Zhang 005 (CoRR, 2020)

Reinforcement Learning with Efficient Active Feature Acquisition. - Haiyan Yin; Yingzhen Li; Sinno Jialin Pan; Cheng Zhang 005; Sebastian Tschiatschek (CoRR, 2020)

Meta-Learning Divergences for Variational Inference. - Ruqi Zhang; Yingzhen Li; Christopher De Sa; Sam Devlin; Cheng Zhang 005 (AISTATS, 2021)

Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification. - Yi Zhu; Ehsan Shareghi; Yingzhen Li; Roi Reichart; Anna Korhonen (EACL, 2021)

Sliced Kernelized Stein Discrepancy. - Wenbo Gong 001; Yingzhen Li; José Miguel Hernández-Lobato (ICLR, 2021)

Active Slices for Sliced Stein Discrepancy. - Wenbo Gong 001; Kaibo Zhang; Yingzhen Li; José Miguel Hernández-Lobato (ICML, 2021)

A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging. - Thomas Henn; Yasukazu Sakamoto; Clément Jacquet; Shunsuke Yoshizawa; Masamichi Andou; Stephen Tchen; Ryosuke Saga; Hiroyuki Ishihara; Katsuhiko Shimizu; Yingzhen Li; Ryutaro Tanno (MICCAI (3), 2021)

Sparse Uncertainty Representation in Deep Learning with Inducing Weights. - Hippolyt Ritter; Martin Kukla; Cheng Zhang 005; Yingzhen Li (NeurIPS, 2021)

Evaluating Approximate Inference in Bayesian Deep Learning. - 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 (NeurIPS (Competition and Demos), 2021)

Learning Sparse Sentence Encoding without Supervision: An Exploration of Sparsity in Variational Autoencoders. - Victor Prokhorov; Yingzhen Li; Ehsan Shareghi; Nigel Collier (RepL4NLP@ACL-IJCNLP, 2021)

Contextual HyperNetworks for Novel Feature Adaptation. - Angus Lamb; Evgeny Saveliev; Yingzhen Li; Sebastian Tschiatschek; Camilla Longden; Simon Woodhead 002; José Miguel Hernández-Lobato; Richard E. Turner; Pashmina Cameron; Cheng Zhang 005 (CoRR, 2021)

Interpreting diffusion score matching using normalizing flow. - Wenbo Gong 001; Yingzhen Li (CoRR, 2021)

Scalable Infomin Learning. - Yanzhi Chen; Weihao Sun; Yingzhen Li; Adrian Weller (NeurIPS, 2022)

Learning Neural Set Functions Under the Optimal Subset Oracle. - Zijing Ou; Tingyang Xu; Qinliang Su; Yingzhen Li; Peilin Zhao; Yatao Bian (NeurIPS, 2022)

Repairing Neural Networks by Leaving the Right Past Behind. - Ryutaro Tanno; Melanie F. Pradier; Aditya V. Nori; Yingzhen Li (NeurIPS, 2022)

Learning Set Functions Under the Optimal Subset Oracle via Equivariant Variational Inference. - Zijing Ou; Tingyang Xu; Qinliang Su; Yingzhen Li; Peilin Zhao; Yatao Bian (CoRR, 2022)

Robust and Adaptive Deep Learning via Bayesian Principles. - Yingzhen Li (AAAI, 2023)

Calibrating Transformers via Sparse Gaussian Processes. - Wenlong Chen; Yingzhen Li (ICLR, 2023)

ESD: Expected Squared Difference as a Tuning-Free Trainable Calibration Measure. - Hee Suk Yoon; Joshua Tian Jin Tee; Eunseop Yoon; Sunjae Yoon; Gwangsu Kim; Yingzhen Li; Chang D. Yoo (ICLR, 2023)

Markovian Gaussian Process Variational Autoencoders. - Harrison Zhu; Carles Balsells Rodas; Yingzhen Li (ICML, 2023)

Energy Discrepancies: A Score-Independent Loss for Energy-Based Models. - Tobias Schröder; Zijing Ou; Jen Lim; Yingzhen Li; Sebastian J. Vollmer; Andrew B. Duncan (NeurIPS, 2023)

On the Identifiability of Markov Switching Models. - Carles Balsells Rodas; Yixin Wang; Yingzhen Li (CoRR, 2023)

Energy Discrepancies: A Score-Independent Loss for Energy-Based Models. - Tobias Schröder; Zijing Ou; Jen Ning Lim; Yingzhen Li; Sebastian J. Vollmer; Andrew B. Duncan (CoRR, 2023)

Training Discrete Energy-Based Models with Energy Discrepancy. - Tobias Schröder; Zijing Ou; Yingzhen Li; Andrew B. Duncan (CoRR, 2023)

Challenges and Perspectives in Deep Generative Modeling (Dagstuhl Seminar 23072). - Vincent Fortuin; Yingzhen Li; Kevin Murphy; Stephan Mandt; Laura Manduchi (Dagstuhl Reports, 2023)

C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion. - Hee Suk Yoon; Eunseop Yoon; Joshua Tian Jin Tee; Mark A. Hasegawa-Johnson; Yingzhen Li; Chang D. Yoo (ICLR, 2024)

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. - Theodore Papamarkou; Maria Skoularidou; Konstantina Palla; Laurence Aitchison; Julyan Arbel; David B. Dunson; Maurizio Filippone; Vincent Fortuin; Philipp Hennig; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (ICML, 2024)

On the Identifiability of Switching Dynamical Systems. - Carles Balsells Rodas; Yixin Wang; Yingzhen Li (ICML, 2024)

Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces. - Tobias Schröder; Zijing Ou; Yingzhen Li; Andrew B. Duncan (NeurIPS, 2024)

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. - Theodore Papamarkou; Maria Skoularidou; Konstantina Palla; Laurence Aitchison; Julyan Arbel; David B. Dunson; Maurizio Filippone; Vincent Fortuin; Philipp Hennig; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (CoRR, 2024)

On the Challenges and Opportunities in Generative AI. - Laura Manduchi; Kushagra Pandey; Robert Bamler; Ryan Cotterell; Sina Däubener; Sophie Fellenz; Asja Fischer; Thomas Gärtner 004; Matthias Kirchler; Marius Kloft; Yingzhen Li; Christoph Lippert; et al. (CoRR, 2024)

Diffusion Model With Optimal Covariance Matching. - Zijing Ou; Mingtian Zhang; Andi Zhang 001; Tim Z. Xiao; Yingzhen Li; David Barber (CoRR, 2024)

Identifying Nonstationary Causal Structures with High-Order Markov Switching Models. - Carles Balsells Rodas; Yixin Wang; Pedro A. M. Mediano; Yingzhen Li (CoRR, 2024)

Mutual Information Multinomial Estimation. - Yanzhi Chen; Zijing Ou; Adrian Weller; Yingzhen Li (CoRR, 2024)

Your Image is Secretly the Last Frame of a Pseudo Video. - Wenlong Chen; Wenlin Chen; Lapo Rastrelli; Yingzhen Li (CoRR, 2024)

Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matching. - Zijing Ou; Mingtian Zhang; Andi Zhang 001; Tim Z. Xiao; Yingzhen Li; David Barber (ICLR, 2025)

Unsupervised Accelerated MRI Reconstruction via Ground-Truth-Free Flow Matching. - Xinzhe Luo; Yingzhen Li; Chen Qin (IPMI (1), 2025)

On the Challenges and Opportunities in Generative AI. - Laura Manduchi; Clara Meister; Kushagra Pandey; Robert Bamler; Ryan Cotterell; Sina Däubener; Sophie Fellenz; Asja Fischer; Thomas Gärtner 004; Matthias Kirchler; Marius Kloft; Yingzhen Li; et al. (Trans. Mach. Learn. Res., 2025)

Neural Flow Samplers with Shortcut Models. - Wuhao Chen; Zijing Ou; Yingzhen Li (CoRR, 2025)

Recurrent Memory for Online Interdomain Gaussian Processes. - Wenlong Chen; Naoki Kiyohara; Harrison Bo Hua Zhu; Yingzhen Li (CoRR, 2025)

Bayesian Computation in Deep Learning. - Wenlong Chen; Bolian Li; Ruqi Zhang; Yingzhen Li (CoRR, 2025)

TabRep: Training Tabular Diffusion Models with a Simple and Effective Continuous Representation. - Jacob Si; Zijing Ou; Mike Qu; Zhengrui Xiang; Yingzhen Li (CoRR, 2025)

Discrete Neural Flow Samplers with Locally Equivariant Transformer. - Zijing Ou; Ruixiang Zhang; Yingzhen Li (CoRR, 2025)

Test-Time Alignment of Discrete Diffusion Models with Sequential Monte Carlo. - Chinmay Pani; Zijing Ou; Yingzhen Li (CoRR, 2025)