ML Anthology

Li, Yingzhen

48 publications

TMLR 2026 TabRep: Training Tabular Diffusion Models with a Simple and Effective Continuous Representation Jacob Si, Zijing Ou, Mike Qu, Zhengrui Xiang, Yingzhen Li
ICML 2025 Causal Discovery from Conditionally Stationary Time Series Carles Balsells-Rodas, Xavier Sumba, Tanmayee Narendra, Ruibo Tu, Gabriele Schweikert, Hedvig Kjellstrom, Yingzhen Li
ICLRW 2025 Causal Representation Learning and Inference via Mixture-Based Priors Avinash Kori, Carles Balsells-Rodas, Ben Glocker, Yingzhen Li, Francesco Locatello
NeurIPS 2025 Compact Memory for Continual Logistic Regression Yohan Jung, Hyungi Lee, Wenlong Chen, Thomas Möllenhoff, Yingzhen Li, Juho Lee, Mohammad Emtiyaz Khan
NeurIPS 2025 Discrete Neural Flow Samplers with Locally Equivariant Transformer Zijing Ou, Ruixiang Zhang, Yingzhen Li
ICLR 2025 Improving Probabilistic Diffusion Models with Optimal Diagonal Covariance Matching Zijing Ou, Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yingzhen Li, David Barber
ICLRW 2025 Neural Flow Samplers with Shortcut Models Wuhao Chen, Zijing Ou, Yingzhen Li
NeurIPS 2025 Neural Stochastic Flows: Solver-Free Modelling and Inference for SDE Solutions Naoki Kiyohara, Edward Johns, Yingzhen Li
TMLR 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, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric 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
NeurIPS 2025 Recurrent Memory for Online Interdomain Gaussian Processes Wenlong Chen, Naoki Kiyohara, Harrison Bo Hua Zhu, Jacob Curran-Sebastian, Samir Bhatt, Yingzhen Li
ICLRW 2025 Recurrent Memory for Online Interdomain Gaussian Processes Wenlong Chen, Naoki Kiyohara, Harrison Bo Hua Zhu, Yingzhen Li
NeurIPS 2025 Variational Uncertainty Decomposition for In-Context Learning I. Shavindra Jayasekera, Jacob Si, Filippo Valdettaro, Wenlong Chen, Aldo A. Faisal, Yingzhen Li
ICLRW 2025 Your Image Is Secretly the Last Frame of a Pseudo Video Wenlong Chen, Wenlin Chen, Lapo Rastrelli, Yingzhen Li
ICLR 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
NeurIPS 2024 Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces Tobias Schröder, Zijing Ou, Yingzhen Li, Andrew Duncan
NeurIPSW 2024 Learning SDE Solutions with Neural Stochastic Flows Naoki Kiyohara, Edward Johns, Yingzhen Li
ICML 2024 On the Identifiability of Switching Dynamical Systems Carles Balsells-Rodas, Yixin Wang, Yingzhen Li
ICML 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 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
ICLR 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
NeurIPS 2023 Energy Discrepancies: A Score-Independent Loss for Energy-Based Models Tobias Schröder, Zijing Ou, Jen Lim, Yingzhen Li, Sebastian Vollmer, Andrew Duncan
ICML 2023 Markovian Gaussian Process Variational Autoencoders Harrison Zhu, Carles Balsells-Rodas, Yingzhen Li
ICMLW 2023 On the Identifiability of Markov Switching Models Carles Balsells-Rodas, Yixin Wang, Yingzhen Li
AAAI 2023 Robust and Adaptive Deep Learning via Bayesian Principles Yingzhen Li
ICMLW 2023 Training Discrete EBMs with Energy Discrepancy Tobias Schröder, Zijing Ou, Yingzhen Li, Andrew B. Duncan
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 Nori, Yingzhen Li
NeurIPS 2022 Scalable Infomin Learning Yanzhi Chen, Weihao Sun, Yingzhen Li, Adrian Weller
AISTATS 2021 Meta-Learning Divergences for Variational Inference Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang
NeurIPSW 2021 Accurate Imputation and Efficient Data Acquisitionwith Transformer-Based VAEs Sarah Lewis, Tatiana Matejovicova, Yingzhen Li, Angus Lamb, Yordan Zaykov, Miltiadis Allamanis, Cheng Zhang
ICML 2021 Active Slices for Sliced Stein Discrepancy Wenbo Gong, Kaibo Zhang, Yingzhen Li, Jose Miguel Hernandez-Lobato
ICMLW 2021 Interpreting Diffusion Score Matching Using Normalizing Flow Wenbo Gong, Yingzhen Li
ICLR 2021 Sliced Kernelized Stein Discrepancy Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
NeurIPS 2021 Sparse Uncertainty Representation in Deep Learning with Inducing Weights Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li
NeurIPS 2020 A Causal View on Robustness of Neural Networks Cheng Zhang, Kun Zhang, Yingzhen Li
NeurIPS 2020 On the Expressiveness of Approximate Inference in Bayesian Neural Networks Andrew Foong, David Burt, Yingzhen Li, Richard Turner
NeurIPSW 2020 Reinforcement Learning with Efficient Active Feature Acquisition Haiyan Yin, Yingzhen Li, Sinno Pan, Cheng Zhang, Sebastian Tschiatschek
ICML 2019 Are Generative Classifiers More Robust to Adversarial Attacks? Yingzhen Li, John Bradshaw, Yash Sharma
NeurIPS 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
ICLR 2019 Meta-Learning for Stochastic Gradient MCMC Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
ICML 2019 Variational Implicit Processes Chao Ma, Yingzhen Li, Jose Miguel Hernandez-Lobato
ICLR 2018 Gradient Estimators for Implicit Models Yingzhen Li, Richard E. Turner
ICLR 2018 Variational Continual Learning Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner
ICML 2017 Dropout Inference in Bayesian Neural Networks with Alpha-Divergences Yingzhen Li, Yarin Gal
ICML 2016 Black-Box Alpha Divergence Minimization Jose Hernandez-Lobato, Yingzhen Li, Mark Rowland, Thang Bui, Daniel Hernandez-Lobato, Richard Turner
ICML 2016 Deep Gaussian Processes for Regression Using Approximate Expectation Propagation Thang Bui, Daniel Hernandez-Lobato, Jose Hernandez-Lobato, Yingzhen Li, Richard Turner
NeurIPS 2016 Rényi Divergence Variational Inference Yingzhen Li, Richard E Turner
NeurIPS 2015 Stochastic Expectation Propagation Yingzhen Li, José Miguel Hernández-Lobato, Richard E Turner