Yingzhen Li - RankMe

109 papers A* 26A 4Journal 66Unranked 12

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Diffusion Alignment Beyond KL: Variance Minimisation as Effective Policy Optimiser. CoRR Zijing Ou, Jacob Si, Junyi Zhu, Ondrej Bohdal, Mete Ozay, Taha Ceritli, Yingzhen Li
2026	J	`jnl`	On the Identifiability of Regime-Switching Models with Multi-Lag Dependencies. CoRR Carles Balsells Rodas, Toshiko Matsui, Pedro A. M. Mediano, Yixin Wang, Yingzhen Li
2026	J	`jnl`	TabRep: Training Tabular Diffusion Models with a Simple and Effective Continuous Representation. Trans. Mach. Learn. Res. Jacob Si, Zijing Ou, Mike Qu, Zhengrui Xiang, Yingzhen Li
2025	J	`jnl`	Bayesian Computation in Deep Learning. CoRR Wenlong Chen, Bolian Li, Ruqi Zhang, Yingzhen Li
2025	A*	`conf`	Causal Discovery from Conditionally Stationary Time Series. ICML Carles Balsells Rodas, Xavier Sumba, Tanmayee Narendra, Ruibo Tu, Gabriele Beate Schweikert, Hedvig Kjellström, Yingzhen Li
2025	J	`jnl`	Compact Memory for Continual Logistic Regression. CoRR Yohan Jung, Hyungi Lee, Wenlong Chen, Thomas Möllenhoff, Yingzhen Li, Juho Lee, Mohammad Emtiyaz Khan
2025	J	`jnl`	Discrete Neural Flow Samplers with Locally Equivariant Transformer. CoRR Zijing Ou, Ruixiang Zhang, Yingzhen Li
2025	J	`jnl`	HiBBO: HiPPO-based Space Consistency for High-dimensional Bayesian Optimisation. CoRR Junyu Xuan, Wenlong Chen, Yingzhen Li
2025	A*	`conf`	Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matching. ICLR Zijing Ou, Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yingzhen Li, David Barber
2025	A	`ed.`	International Conference on Artificial Intelligence and Statistics, AISTATS 2025, Mai Khao, Thailand, 3-5 May 2025. AISTATS Yingzhen Li, Stephan Mandt, Shipra Agrawal, Mohammad Emtiyaz Khan
2025	J	`jnl`	Memory-Driven Self-Improvement for Decision Making with Large Language Models. CoRR Xue Yan, Zijing Ou, Mengyue Yang, Yan Song, Haifeng Zhang, Yingzhen Li, Jun Wang
2025	J	`jnl`	NMIRacle: Multi-modal Generative Molecular Elucidation from IR and NMR Spectra. CoRR Federico Ottomano, Yingzhen Li, Alex M. Ganose
2025	J	`jnl`	Neural Flow Samplers with Shortcut Models. CoRR Wuhao Chen, Zijing Ou, Yingzhen Li
2025	J	`jnl`	Neural Stochastic Flows: Solver-Free Modelling and Inference for SDE Solutions. CoRR Naoki Kiyohara, Edward Johns, Yingzhen Li
2025	J	`jnl`	On the Challenges and Opportunities in Generative AI. Trans. Mach. Learn. Res. 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 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
2025	J	`jnl`	Recurrent Memory for Online Interdomain Gaussian Processes. CoRR Wenlong Chen, Naoki Kiyohara, Harrison Bo Hua Zhu, Yingzhen Li
2025	J	`jnl`	TabRAG: Tabular Document Retrieval via Structured Language Representations. CoRR Jacob Si, Mike Qu, Michelle Lee, Yingzhen Li
2025	J	`jnl`	TabRep: Training Tabular Diffusion Models with a Simple and Effective Continuous Representation. CoRR Jacob Si, Zijing Ou, Mike Qu, Zhengrui Xiang, Yingzhen Li
2025	J	`jnl`	Test-Time Alignment of Discrete Diffusion Models with Sequential Monte Carlo. CoRR Chinmay Pani, Zijing Ou, Yingzhen Li
2025	—	`conf`	Unsupervised Accelerated MRI Reconstruction via Ground-Truth-Free Flow Matching. IPMI (1) Xinzhe Luo, Yingzhen Li, Chen Qin
2025	J	`jnl`	Variational Uncertainty Decomposition for In-Context Learning. CoRR I. Shavindra Jayasekera, Jacob Si, Filippo Valdettaro, Wenlong Chen, A. Aldo Faisal, Yingzhen Li
2024	A*	`conf`	C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion. ICLR Hee Suk Yoon, Eunseop Yoon, Joshua Tian Jin Tee, Mark A. Hasegawa-Johnson, Yingzhen Li, Chang D. Yoo
2024	J	`jnl`	C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion. CoRR Hee Suk Yoon, Eunseop Yoon, Joshua Tian Jin Tee, Mark Hasegawa-Johnson, Yingzhen Li, Chang D. Yoo
2024	J	`jnl`	Diffusion Model With Optimal Covariance Matching. CoRR Zijing Ou, Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yingzhen Li, David Barber
2024	A*	`conf`	Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces. NeurIPS Tobias Schröder, Zijing Ou, Yingzhen Li, Andrew B. Duncan
2024	J	`jnl`	Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces. CoRR Tobias Schröder, Zijing Ou, Yingzhen Li, Andrew B. Duncan
2024	J	`jnl`	Identifying Nonstationary Causal Structures with High-Order Markov Switching Models. CoRR Carles Balsells Rodas, Yixin Wang, Pedro A. M. Mediano, Yingzhen Li
2024	A	`ed.`	International Conference on Artificial Intelligence and Statistics, 2-4 May 2024, Palau de Congressos, Valencia, Spain. AISTATS Sanjoy Dasgupta, Stephan Mandt, Yingzhen Li
2024	J	`jnl`	Mutual Information Multinomial Estimation. CoRR Yanzhi Chen, Zijing Ou, Adrian Weller, Yingzhen Li
2024	J	`jnl`	On the Challenges and Opportunities in Generative AI. CoRR 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
2024	A*	`conf`	On the Identifiability of Switching Dynamical Systems. ICML Carles Balsells Rodas, Yixin Wang, Yingzhen Li
2024	J	`jnl`	Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR 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
2024	A*	`conf`	Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. ICML 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
2024	J	`jnl`	Your Image is Secretly the Last Frame of a Pseudo Video. CoRR Wenlong Chen, Wenlin Chen, Lapo Rastrelli, Yingzhen Li
2023	A*	`conf`	Calibrating Transformers via Sparse Gaussian Processes. ICLR Wenlong Chen, Yingzhen Li
2023	J	`jnl`	Calibrating Transformers via Sparse Gaussian Processes. CoRR Wenlong Chen, Yingzhen Li
2023	J	`jnl`	Challenges and Perspectives in Deep Generative Modeling (Dagstuhl Seminar 23072). Dagstuhl Reports Vincent Fortuin, Yingzhen Li, Kevin Murphy, Stephan Mandt, Laura Manduchi
2023	A*	`conf`	ESD: Expected Squared Difference as a Tuning-Free Trainable Calibration Measure. ICLR Hee Suk Yoon, Joshua Tian Jin Tee, Eunseop Yoon, Sunjae Yoon, Gwangsu Kim, Yingzhen Li, Chang D. Yoo
2023	J	`jnl`	ESD: Expected Squared Difference as a Tuning-Free Trainable Calibration Measure. CoRR Hee Suk Yoon, Joshua Tian Jin Tee, Eunseop Yoon, Sunjae Yoon, Gwangsu Kim, Yingzhen Li, Chang D. Yoo
2023	A*	`conf`	Energy Discrepancies: A Score-Independent Loss for Energy-Based Models. NeurIPS Tobias Schröder, Zijing Ou, Jen Lim, Yingzhen Li, Sebastian J. Vollmer, Andrew B. Duncan
2023	J	`jnl`	Energy Discrepancies: A Score-Independent Loss for Energy-Based Models. CoRR Tobias Schröder, Zijing Ou, Jen Ning Lim, Yingzhen Li, Sebastian J. Vollmer, Andrew B. Duncan
2023	A*	`conf`	Markovian Gaussian Process Variational Autoencoders. ICML Harrison Zhu, Carles Balsells Rodas, Yingzhen Li
2023	J	`jnl`	On the Identifiability of Markov Switching Models. CoRR Carles Balsells Rodas, Yixin Wang, Yingzhen Li
2023	A*	`conf`	Robust and Adaptive Deep Learning via Bayesian Principles. AAAI Yingzhen Li
2023	J	`jnl`	Scalable Infomin Learning. CoRR Yanzhi Chen, Weihao Sun, Yingzhen Li, Adrian Weller
2023	J	`jnl`	Training Discrete Energy-Based Models with Energy Discrepancy. CoRR Tobias Schröder, Zijing Ou, Yingzhen Li, Andrew B. Duncan
2022	A*	`conf`	Learning Neural Set Functions Under the Optimal Subset Oracle. NeurIPS Zijing Ou, Tingyang Xu, Qinliang Su, Yingzhen Li, Peilin Zhao, Yatao Bian
2022	J	`jnl`	Learning Set Functions Under the Optimal Subset Oracle via Equivariant Variational Inference. CoRR Zijing Ou, Tingyang Xu, Qinliang Su, Yingzhen Li, Peilin Zhao, Yatao Bian
2022	J	`jnl`	Markovian Gaussian Process Variational Autoencoders. CoRR Harrison Zhu, Carles Balsells Rodas, Yingzhen Li
2022	A*	`conf`	Repairing Neural Networks by Leaving the Right Past Behind. NeurIPS Ryutaro Tanno, Melanie F. Pradier, Aditya V. Nori, Yingzhen Li
2022	J	`jnl`	Repairing Neural Networks by Leaving the Right Past Behind. CoRR Ryutaro Tanno, Melanie F. Pradier, Aditya V. Nori, Yingzhen Li
2022	A*	`conf`	Scalable Infomin Learning. NeurIPS Yanzhi Chen, Weihao Sun, Yingzhen Li, Adrian Weller
2021	—	`conf`	A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging. MICCAI (3) Thomas Henn, Yasukazu Sakamoto, Clément Jacquet, Shunsuke Yoshizawa, Masamichi Andou, Stephen Tchen, Ryosuke Saga, Hiroyuki Ishihara, Katsuhiko Shimizu, Yingzhen Li, Ryutaro Tanno
2021	J	`jnl`	A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging. CoRR Thomas Henn, Yasukazu Sakamoto, Clément Jacquet, Shunsuke Yoshizawa, Masamichi Andou, Stephen Tchen, Ryosuke Saga, Hiroyuki Ishihara, Katsuhiko Shimizu, Yingzhen Li, Ryutaro Tanno
2021	A*	`conf`	Active Slices for Sliced Stein Discrepancy. ICML Wenbo Gong, Kaibo Zhang, Yingzhen Li, José Miguel Hernández-Lobato
2021	J	`jnl`	Active Slices for Sliced Stein Discrepancy. CoRR Wenbo Gong, Kaibo Zhang, Yingzhen Li, José Miguel Hernández-Lobato
2021	A	`conf`	Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification. EACL Yi Zhu, Ehsan Shareghi, Yingzhen Li, Roi Reichart, Anna Korhonen
2021	J	`jnl`	Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification. CoRR Yi Zhu, Ehsan Shareghi, Yingzhen Li, Roi Reichart, Anna Korhonen
2021	J	`jnl`	Contextual HyperNetworks for Novel Feature Adaptation. CoRR Angus Lamb, Evgeny Saveliev, Yingzhen Li, Sebastian Tschiatschek, Camilla Longden, Simon Woodhead, José Miguel Hernández-Lobato, Richard E. Turner, Pashmina Cameron, Cheng Zhang
2021	—	`conf`	Evaluating Approximate Inference in Bayesian Deep Learning. NeurIPS (Competition and Demos) 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
2021	J	`jnl`	Interpreting diffusion score matching using normalizing flow. CoRR Wenbo Gong, Yingzhen Li
2021	—	`conf`	Learning Sparse Sentence Encoding without Supervision: An Exploration of Sparsity in Variational Autoencoders. RepL4NLP@ACL-IJCNLP Victor Prokhorov, Yingzhen Li, Ehsan Shareghi, Nigel Collier
2021	A	`conf`	Meta-Learning Divergences for Variational Inference. AISTATS Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang
2021	A*	`conf`	Sliced Kernelized Stein Discrepancy. ICLR Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
2021	A*	`conf`	Sparse Uncertainty Representation in Deep Learning with Inducing Weights. NeurIPS Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li
2021	J	`jnl`	Sparse Uncertainty Representation in Deep Learning with Inducing Weights. CoRR Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li
2020	A*	`conf`	A Causal View on Robustness of Neural Networks. NeurIPS Cheng Zhang, Kun Zhang, Yingzhen Li
2020	J	`jnl`	A Causal View on Robustness of Neural Networks. CoRR Cheng Zhang, Kun Zhang, Yingzhen Li
2020	J	`jnl`	A Study on Efficiency in Continual Learning Inspired by Human Learning. CoRR Philip J. Ball, Yingzhen Li, Angus Lamb, Cheng Zhang
2020	J	`jnl`	Hierarchical Sparse Variational Autoencoder for Text Encoding. CoRR Victor Prokhorov, Yingzhen Li, Ehsan Shareghi, Nigel Collier
2020	J	`jnl`	Interpreting Spatially Infinite Generative Models. CoRR Chaochao Lu, Richard E. Turner, Yingzhen Li, Nate Kushman
2020	J	`jnl`	Inverse Graphics GAN: Learning to Generate 3D Shapes from Unstructured 2D Data. CoRR Sebastian Lunz, Yingzhen Li, Andrew W. Fitzgibbon, Nate Kushman
2020	J	`jnl`	Meta-Learning for Variational Inference. CoRR Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang
2020	A*	`conf`	On the Expressiveness of Approximate Inference in Bayesian Neural Networks. NeurIPS Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner
2020	J	`jnl`	Reinforcement Learning with Efficient Active Feature Acquisition. CoRR Haiyan Yin, Yingzhen Li, Sinno Jialin Pan, Cheng Zhang, Sebastian Tschiatschek
2020	J	`jnl`	Sliced Kernelized Stein Discrepancy. CoRR Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
2019	J	`jnl`	'In-Between' Uncertainty in Bayesian Neural Networks. CoRR Andrew Y. K. Foong, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner
2019	A*	`conf`	Are Generative Classifiers More Robust to Adversarial Attacks? ICML Yingzhen Li, John Bradshaw, Yash Sharma
2019	—	`conf`	Bayesian Learning for Neural Dependency Parsing. NAACL-HLT (1) Ehsan Shareghi, Yingzhen Li, Yi Zhu, Roi Reichart, Anna Korhonen
2019	A*	`conf`	Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. NeurIPS Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann
2019	J	`jnl`	Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. CoRR Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann
2019	—	`conf`	HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals. AABI Chao Ma, Sebastian Tschiatschek, Yingzhen Li, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang
2019	J	`jnl`	Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care. CoRR Anna-Lena Popkes, Hiske Overweg, Ari Ercole, Yingzhen Li, José Miguel Hernández-Lobato, Yordan Zaykov, Cheng Zhang
2019	—	`conf`	Meta-Learning For Stochastic Gradient MCMC. ICLR (Poster) Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
2019	—	`conf`	On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation. NGT@EMNLP-IJCNLP Victor Prokhorov, Ehsan Shareghi, Yingzhen Li, Mohammad Taher Pilehvar, Nigel Collier
2019	J	`jnl`	On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation. CoRR Victor Prokhorov, Ehsan Shareghi, Yingzhen Li, Mohammad Taher Pilehvar, Nigel Collier
2019	J	`jnl`	Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks. CoRR Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner
2019	A*	`conf`	Variational Implicit Processes. ICML Chao Ma, Yingzhen Li, José Miguel Hernández-Lobato
2018	J	`jnl`	A Deep Generative Model for Disentangled Representations of Sequential Data. CoRR Yingzhen Li, Stephan Mandt
2018	—	`—`	Approximate inference: new visions. Yingzhen Li
2018	J	`jnl`	Are Generative Classifiers More Robust to Adversarial Attacks? CoRR Yingzhen Li
2018	A*	`conf`	Disentangled Sequential Autoencoder. ICML Yingzhen Li, Stephan Mandt
2018	—	`conf`	Gradient Estimators for Implicit Models. ICLR (Poster) Yingzhen Li, Richard E. Turner
2018	J	`jnl`	Meta-Learning for Stochastic Gradient MCMC. CoRR Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
2018	—	`conf`	Variational Continual Learning. ICLR (Poster) Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner
2018	J	`jnl`	Variational Implicit Processes. CoRR Chao Ma, Yingzhen Li, José Miguel Hernández-Lobato
2017	J	`jnl`	Approximate Inference with Amortised MCMC. CoRR Yingzhen Li, Richard E. Turner, Qiang Liu
2017	A*	`conf`	Dropout Inference in Bayesian Neural Networks with Alpha-divergences. ICML Yingzhen Li, Yarin Gal
2017	J	`jnl`	Dropout Inference in Bayesian Neural Networks with Alpha-divergences. CoRR Yingzhen Li, Yarin Gal
2017	J	`jnl`	Gradient Estimators for Implicit Models. CoRR Yingzhen Li, Richard E. Turner
2017	J	`jnl`	Variational Continual Learning. CoRR Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner
2016	A*	`conf`	Black-Box Alpha Divergence Minimization. ICML José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Thang D. Bui, Daniel Hernández-Lobato, Richard E. Turner
2016	A*	`conf`	Deep Gaussian Processes for Regression using Approximate Expectation Propagation. ICML Thang D. Bui, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li, Richard E. Turner
2016	J	`jnl`	Deep Gaussian Processes for Regression using Approximate Expectation Propagation. CoRR Thang D. Bui, Daniel Hernández-Lobato, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner
2016	—	`conf`	Rényi Divergence Variational Inference. NIPS Yingzhen Li, Richard E. Turner
2016	J	`jnl`	Variational Inference with Rényi Divergence. CoRR Yingzhen Li, Richard E. Turner
2015	—	`conf`	Stochastic Expectation Propagation. NIPS Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner
2015	J	`jnl`	Stochastic Expectation Propagation. CoRR Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner
2012	J	`jnl`	Generating ordered list of Recommended Items: a Hybrid Recommender System of Microblog CoRR Yingzhen Li, Ye Zhang