Yingzhen Li

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 77

Top Topics

Gaussian Process

Variational Inference

Top Venues

Publications

Carles Balsells Rodas, Yixin Wang, Pedro A. M. Mediano, Yingzhen Li
Identifying Nonstationary Causal Structures with High-Order Markov Switching Models. CoRR (2024)
Hee Suk Yoon, Eunseop Yoon, Joshua Tian Jin Tee, Mark Hasegawa-Johnson, Yingzhen Li, Chang D. Yoo
C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion. CoRR (2024)
Zijing Ou, Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yingzhen Li, David Barber
Diffusion Model With Optimal Covariance Matching. CoRR (2024)
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, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR (2024)
Laura Manduchi, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric T. Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E. Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin
On the Challenges and Opportunities in Generative AI. CoRR (2024)
Hee Suk Yoon, Eunseop Yoon, Joshua Tian Jin Tee, Mark A. Hasegawa-Johnson, Yingzhen Li, Chang D. Yoo
C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion. ICLR (2024)
Yingzhen Li
Robust and Adaptive Deep Learning via Bayesian Principles. AAAI (2023)
Harrison Zhu, Carles Balsells Rodas, Yingzhen Li
Markovian Gaussian Process Variational Autoencoders. ICML (2023)
Tobias Schröder, Zijing Ou, Jen Ning Lim, Yingzhen Li, Sebastian J. Vollmer, Andrew B. Duncan
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models. CoRR (2023)
Tobias Schröder, Zijing Ou, Yingzhen Li, Andrew B. Duncan
Training Discrete Energy-Based Models with Energy Discrepancy. CoRR (2023)
Hee Suk Yoon, Joshua Tian Jin Tee, Eunseop Yoon, Sunjae Yoon, Gwangsu Kim, Yingzhen Li, Chang D. Yoo
ESD: Expected Squared Difference as a Tuning-Free Trainable Calibration Measure. ICLR (2023)
Tobias Schröder, Zijing Ou, Jen Lim, Yingzhen Li, Sebastian J. Vollmer, Andrew Duncan
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models. NeurIPS (2023)
Wenlong Chen, Yingzhen Li
Calibrating Transformers via Sparse Gaussian Processes. CoRR (2023)
Carles Balsells Rodas, Yixin Wang, Yingzhen Li
On the Identifiability of Markov Switching Models. CoRR (2023)
Yanzhi Chen, Weihao Sun, Yingzhen Li, Adrian Weller
Scalable Infomin Learning. CoRR (2023)
Vincent Fortuin, Yingzhen Li, Kevin Murphy, Stephan Mandt, Laura Manduchi
Challenges and Perspectives in Deep Generative Modeling (Dagstuhl Seminar 23072). Dagstuhl Reports 13 (2) (2023)
Hee Suk Yoon, Joshua Tian Jin Tee, Eunseop Yoon, Sunjae Yoon, Gwangsu Kim, Yingzhen Li, Chang D. Yoo
ESD: Expected Squared Difference as a Tuning-Free Trainable Calibration Measure. CoRR (2023)
Wenlong Chen, Yingzhen Li
Calibrating Transformers via Sparse Gaussian Processes. ICLR (2023)
Ryutaro Tanno, Melanie F. Pradier, Aditya V. Nori, Yingzhen Li
Repairing Neural Networks by Leaving the Right Past Behind. CoRR (2022)
Zijing Ou, Tingyang Xu, Qinliang Su, Yingzhen Li, Peilin Zhao, Yatao Bian
Learning Neural Set Functions Under the Optimal Subset Oracle. NeurIPS (2022)
Yanzhi Chen, Weihao Sun, Yingzhen Li, Adrian Weller
Scalable Infomin Learning. NeurIPS (2022)
Harrison Zhu, Carles Balsells Rodas, Yingzhen Li
Markovian Gaussian Process Variational Autoencoders. CoRR (2022)
Ryutaro Tanno, Melanie F. Pradier, Aditya V. Nori, Yingzhen Li
Repairing Neural Networks by Leaving the Right Past Behind. NeurIPS (2022)
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 (2022)
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 (2021)
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)
Yi Zhu, Ehsan Shareghi, Yingzhen Li, Roi Reichart, Anna Korhonen
Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification. CoRR (2021)
Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
Sliced Kernelized Stein Discrepancy. ICLR (2021)
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 (2021)
Wenbo Gong, Yingzhen Li
Interpreting diffusion score matching using normalizing flow. CoRR (2021)
Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang
Meta-Learning Divergences for Variational Inference. AISTATS (2021)
Wenbo Gong, Kaibo Zhang, Yingzhen Li, José Miguel Hernández-Lobato
Active Slices for Sliced Stein Discrepancy. CoRR (2021)
Wenbo Gong, Kaibo Zhang, Yingzhen Li, José Miguel Hernández-Lobato
Active Slices for Sliced Stein Discrepancy. ICML (2021)
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)
Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li
Sparse Uncertainty Representation in Deep Learning with Inducing Weights. NeurIPS (2021)
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)
Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li
Sparse Uncertainty Representation in Deep Learning with Inducing Weights. CoRR (2021)
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)
Cheng Zhang, Kun Zhang, Yingzhen Li
A Causal View on Robustness of Neural Networks. NeurIPS (2020)
Sebastian Lunz, Yingzhen Li, Andrew W. Fitzgibbon, Nate Kushman
Inverse Graphics GAN: Learning to Generate 3D Shapes from Unstructured 2D Data. CoRR (2020)
Haiyan Yin, Yingzhen Li, Sinno Jialin Pan, Cheng Zhang, Sebastian Tschiatschek
Reinforcement Learning with Efficient Active Feature Acquisition. CoRR (2020)
Cheng Zhang, Kun Zhang, Yingzhen Li
A Causal View on Robustness of Neural Networks. CoRR (2020)
Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
Sliced Kernelized Stein Discrepancy. CoRR (2020)
Chaochao Lu, Richard E. Turner, Yingzhen Li, Nate Kushman
Interpreting Spatially Infinite Generative Models. CoRR (2020)
Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang
Meta-Learning for Variational Inference. CoRR (2020)
Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner
On the Expressiveness of Approximate Inference in Bayesian Neural Networks. NeurIPS (2020)
Philip J. Ball, Yingzhen Li, Angus Lamb, Cheng Zhang
A Study on Efficiency in Continual Learning Inspired by Human Learning. CoRR (2020)
Victor Prokhorov, Yingzhen Li, Ehsan Shareghi, Nigel Collier
Hierarchical Sparse Variational Autoencoder for Text Encoding. CoRR (2020)
Chao Ma, Yingzhen Li, José Miguel Hernández-Lobato
Variational Implicit Processes. ICML (2019)
Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
Meta-Learning For Stochastic Gradient MCMC. ICLR (Poster) (2019)
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 (2019)
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)
Ehsan Shareghi, Yingzhen Li, Yi Zhu, Roi Reichart, Anna Korhonen
Bayesian Learning for Neural Dependency Parsing. NAACL-HLT (1) (2019)
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)
Andrew Y. K. Foong, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner
'In-Between' Uncertainty in Bayesian Neural Networks. CoRR (2019)
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 (2019)
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)
Yingzhen Li, John Bradshaw, Yash Sharma
Are Generative Classifiers More Robust to Adversarial Attacks? ICML (2019)
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 (2019)
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 (2019)
Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
Meta-Learning for Stochastic Gradient MCMC. CoRR (2018)
Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner
Variational Continual Learning. ICLR (Poster) (2018)
Yingzhen Li, Richard E. Turner
Gradient Estimators for Implicit Models. ICLR (Poster) (2018)
Yingzhen Li, Stephan Mandt
Disentangled Sequential Autoencoder. ICML (2018)
Chao Ma, Yingzhen Li, José Miguel Hernández-Lobato
Variational Implicit Processes. CoRR (2018)
Yingzhen Li
Are Generative Classifiers More Robust to Adversarial Attacks? CoRR (2018)
Yingzhen Li, Stephan Mandt
A Deep Generative Model for Disentangled Representations of Sequential Data. CoRR (2018)
Yingzhen Li, Richard E. Turner, Qiang Liu
Approximate Inference with Amortised MCMC. CoRR (2017)
Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner
Variational Continual Learning. CoRR (2017)
Yingzhen Li, Yarin Gal
Dropout Inference in Bayesian Neural Networks with Alpha-divergences. ICML (2017)
Yingzhen Li, Yarin Gal
Dropout Inference in Bayesian Neural Networks with Alpha-divergences. CoRR (2017)
Yingzhen Li, Richard E. Turner
Gradient Estimators for Implicit Models. CoRR (2017)
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 (2016)
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)
Yingzhen Li, Richard E. Turner
Variational Inference with Rényi Divergence. CoRR (2016)
Yingzhen Li, Richard E. Turner
Rényi Divergence Variational Inference. NIPS (2016)
José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Thang D. Bui, Daniel Hernández-Lobato, Richard E. Turner
Black-Box Alpha Divergence Minimization. ICML (2016)
Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner
Stochastic Expectation Propagation. NIPS (2015)
Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner
Stochastic Expectation Propagation. CoRR (2015)
Yingzhen Li, Ye Zhang
Generating ordered list of Recommended Items: a Hybrid Recommender System of Microblog CoRR (2012)