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2020 – today
- 2024
[c36]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- [c35]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: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. ICML 2024 - [c34]Carles Balsells Rodas, Yixin Wang, Yingzhen Li:
On the Identifiability of Switching Dynamical Systems. ICML 2024 - [e1]Sanjoy Dasgupta, Stephan Mandt, Yingzhen Li:
International Conference on Artificial Intelligence and Statistics, 2-4 May 2024, Palau de Congressos, Valencia, Spain. Proceedings of Machine Learning Research 238, PMLR 2024 [contents] - [i48]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 abs/2402.00809 (2024) - [i47]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 abs/2403.00025 (2024) - [i46]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 abs/2403.14119 (2024) - [i45]Zijing Ou, Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yingzhen Li, David Barber:
Diffusion Model With Optimal Covariance Matching. CoRR abs/2406.10808 (2024) - [i44]Carles Balsells Rodas, Yixin Wang, Pedro A. M. Mediano, Yingzhen Li:
Identifying Nonstationary Causal Structures with High-Order Markov Switching Models. CoRR abs/2406.17698 (2024) - [i43]Yanzhi Chen, Zijing Ou, Adrian Weller, Yingzhen Li:
Mutual Information Multinomial Estimation. CoRR abs/2408.09377 (2024) - [i42]Wenlong Chen, Wenlin Chen, Lapo Rastrelli, Yingzhen Li:
Your Image is Secretly the Last Frame of a Pseudo Video. CoRR abs/2410.20158 (2024) - 2023
- [c33]Yingzhen Li:
Robust and Adaptive Deep Learning via Bayesian Principles. AAAI 2023: 15446 - [c32]Wenlong Chen, Yingzhen Li:
Calibrating Transformers via Sparse Gaussian Processes. ICLR 2023 - [c31]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 - [c30]Harrison Zhu, Carles Balsells Rodas, Yingzhen Li:
Markovian Gaussian Process Variational Autoencoders. ICML 2023: 42938-42961 - [c29]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 - [i41]Yanzhi Chen, Weihao Sun, Yingzhen Li, Adrian Weller:
Scalable Infomin Learning. CoRR abs/2302.10701 (2023) - [i40]Wenlong Chen, Yingzhen Li:
Calibrating Transformers via Sparse Gaussian Processes. CoRR abs/2303.02444 (2023) - [i39]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 abs/2303.02472 (2023) - [i38]Carles Balsells Rodas, Yixin Wang, Yingzhen Li:
On the Identifiability of Markov Switching Models. CoRR abs/2305.15925 (2023) - [i37]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 abs/2307.06431 (2023) - [i36]Tobias Schröder, Zijing Ou, Yingzhen Li, Andrew B. Duncan:
Training Discrete Energy-Based Models with Energy Discrepancy. CoRR abs/2307.07595 (2023) - [i35]Vincent Fortuin, Yingzhen Li, Kevin Murphy, Stephan Mandt, Laura Manduchi:
Challenges and Perspectives in Deep Generative Modeling (Dagstuhl Seminar 23072). Dagstuhl Reports 13(2): 47-70 (2023) - 2022
- [c28]Yanzhi Chen, Weihao Sun, Yingzhen Li, Adrian Weller:
Scalable Infomin Learning. NeurIPS 2022 - [c27]Zijing Ou, Tingyang Xu, Qinliang Su, Yingzhen Li, Peilin Zhao, Yatao Bian:
Learning Neural Set Functions Under the Optimal Subset Oracle. NeurIPS 2022 - [c26]Ryutaro Tanno, Melanie F. Pradier, Aditya V. Nori, Yingzhen Li:
Repairing Neural Networks by Leaving the Right Past Behind. NeurIPS 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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