Yingzhen Li

According to our database, Yingzhen Li authored at least 63 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Mutual Information Multinomial Estimation.

Yanzhi Chen, Zijing Ou, Adrian Weller, Yingzhen Li

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

Diffusion Model With Optimal Covariance Matching.

Zijing Ou, Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yingzhen Li, David Barber

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, 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

CoRR, 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, 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

CoRR, 2024

On the Identifiability of Switching Dynamical Systems.

Carles Balsells Rodas, Yixin Wang, Yingzhen Li

Proceedings of the Forty-first International Conference on Machine Learning, 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, 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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Challenges and Perspectives in Deep Generative Modeling (Dagstuhl Seminar 23072).

Vincent Fortuin, Yingzhen Li, Kevin Murphy, Stephan Mandt, Laura Manduchi

Dagstuhl Reports, February, 2023

Training Discrete Energy-Based Models with Energy Discrepancy.

Tobias Schröder, Zijing Ou, Yingzhen Li, Andrew B. Duncan

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

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 Lim, Yingzhen Li, Sebastian J. Vollmer, Andrew Duncan

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Markovian Gaussian Process Variational Autoencoders.

Harrison Zhu, Carles Balsells Rodas, Yingzhen Li

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Calibrating Transformers via Sparse Gaussian Processes.

Wenlong Chen, Yingzhen Li

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Robust and Adaptive Deep Learning via Bayesian Principles.

Yingzhen Li

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

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

Repairing Neural Networks by Leaving the Right Past Behind.

Ryutaro Tanno, Melanie F. Pradier, Aditya V. Nori, Yingzhen Li

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning Neural Set Functions Under the Optimal Subset Oracle.

Zijing Ou, Tingyang Xu, Qinliang Su, Yingzhen Li, Peilin Zhao, Yatao Bian

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Scalable Infomin Learning.

Yanzhi Chen, Weihao Sun, Yingzhen Li, Adrian Weller

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Interpreting diffusion score matching using normalizing flow.

Wenbo Gong, Yingzhen Li

CoRR, 2021

Contextual HyperNetworks for Novel Feature Adaptation.

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

CoRR, 2021

Learning Sparse Sentence Encoding without Supervision: An Exploration of Sparsity in Variational Autoencoders.

Victor Prokhorov, Yingzhen Li, Ehsan Shareghi, Nigel Collier

Proceedings of the 6th Workshop on Representation Learning for NLP, 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

Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, 2021

Sparse Uncertainty Representation in Deep Learning with Inducing Weights.

Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 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

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021

Active Slices for Sliced Stein Discrepancy.

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

Proceedings of the 38th International Conference on Machine Learning, 2021

Sliced Kernelized Stein Discrepancy.

Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato

Proceedings of the 9th International Conference on Learning Representations, 2021

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

Yi Zhu, Ehsan Shareghi, Yingzhen Li, Roi Reichart, Anna Korhonen

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2021

Meta-Learning Divergences for Variational Inference.

Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Reinforcement Learning with Efficient Active Feature Acquisition.

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

CoRR, 2020

A Study on Efficiency in Continual Learning Inspired by Human Learning.

Philip J. Ball, Yingzhen Li, Angus Lamb, Cheng Zhang

CoRR, 2020

Hierarchical Sparse Variational Autoencoder for Text Encoding.

Victor Prokhorov, Yingzhen Li, Ehsan Shareghi, Nigel Collier

CoRR, 2020

Interpreting Spatially Infinite Generative Models.

Chaochao Lu, Richard E. Turner, Yingzhen Li, Nate Kushman

CoRR, 2020

Meta-Learning for Variational Inference.

Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang

CoRR, 2020

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

Sebastian Lunz, Yingzhen Li, Andrew W. Fitzgibbon, Nate Kushman

CoRR, 2020

On the Expressiveness of Approximate Inference in Bayesian Neural Networks.

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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A Causal View on Robustness of Neural Networks.

Cheng Zhang, Kun Zhang, Yingzhen Li

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

'In-Between' Uncertainty in Bayesian Neural Networks.

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

CoRR, 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

CoRR, 2019

Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck.

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

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Bayesian Learning for Neural Dependency Parsing.

Ehsan Shareghi, Yingzhen Li, Yi Zhu, Roi Reichart, Anna Korhonen

Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019

Variational Implicit Processes.

Chao Ma, Yingzhen Li, José Miguel Hernández-Lobato

Proceedings of the 36th International Conference on Machine Learning, 2019

Are Generative Classifiers More Robust to Adversarial Attacks?

Yingzhen Li, John Bradshaw, Yash Sharma

Proceedings of the 36th International Conference on Machine Learning, 2019

Meta-Learning For Stochastic Gradient MCMC.

Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato

Proceedings of the 7th International Conference on Learning Representations, 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

Proceedings of the 3rd Workshop on Neural Generation and Translation@EMNLP-IJCNLP 2019, 2019

HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals.

Chao Ma, Sebastian Tschiatschek, Yingzhen Li, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang

Proceedings of the Symposium on Advances in Approximate Bayesian Inference, 2019

2018

Approximate inference: new visions.

Yingzhen Li

PhD thesis, 2018

A Deep Generative Model for Disentangled Representations of Sequential Data.

Yingzhen Li, Stephan Mandt

CoRR, 2018

Are Generative Classifiers More Robust to Adversarial Attacks?

Yingzhen Li

CoRR, 2018

Disentangled Sequential Autoencoder.

Yingzhen Li, Stephan Mandt

Proceedings of the 35th International Conference on Machine Learning, 2018

Variational Continual Learning.

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

Proceedings of the 6th International Conference on Learning Representations, 2018

Gradient Estimators for Implicit Models.

Yingzhen Li, Richard E. Turner

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Approximate Inference with Amortised MCMC.

Yingzhen Li, Richard E. Turner, Qiang Liu

CoRR, 2017

Dropout Inference in Bayesian Neural Networks with Alpha-divergences.

Yingzhen Li, Yarin Gal

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Variational Inference with Rényi Divergence.

Yingzhen Li, Richard E. Turner

CoRR, 2016

Rényi Divergence Variational Inference.

Yingzhen Li, Richard E. Turner

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Black-Box Alpha Divergence Minimization.

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

Proceedings of the 33nd International Conference on Machine Learning, 2016

Deep Gaussian Processes for Regression using Approximate Expectation Propagation.

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

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Stochastic Expectation Propagation.

Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2012

Generating ordered list of Recommended Items: a Hybrid Recommender System of Microblog

Yingzhen Li, Ye Zhang

CoRR, 2012