Yingzhen Li

发表

Black-box α-divergence minimization

Richard E. Turner, José Miguel Hernández-Lobato, Mark Rowland, 2016, ICML 2016.

Gradient Estimators for Implicit Models pdf

Richard E. Turner, Yingzhen Li, Yingzhen Li, 2017, ICLR.

Stochastic Expectation Propagation

Richard E. Turner, José Miguel Hernández-Lobato, Yingzhen Li, 2015, NIPS.

Rényi Divergence Variational Inference

Richard E. Turner, Yingzhen Li, Yingzhen Li, 2016, NIPS.

Dropout Inference in Bayesian Neural Networks with Alpha-divergences

Yarin Gal, Yingzhen Li, Y. Gal, 2017, ICML.

ETA-L EARNING FOR S TOCHASTIC G RADIENT MCMC

José Miguel Hernández-Lobato, Yingzhen Li, Wenbo Gong, 2019 .

On the Expressiveness of Approximate Inference in Bayesian Neural Networks

Richard E. Turner, Andrew Y. K. Foong, David R. Burt, 2019, NeurIPS.

Yingzhen Li Topics in Approximate Inference 0 Front Matter

Yingzhen Li, 2017 .

Approximate Gradient Descent for Training Implicit Generative Models

Yingzhen Li, 2017 .

M L ] 1 0 N ov 2 01 5 Black-box α-divergence Minimization

Richard E. Turner, José Miguel Hernández-Lobato, Yingzhen Li, 2015 .

Variational Continual Learning pdf

Richard E. Turner, Thang D. Bui, Cuong V. Nguyen, 2017, ICLR.

Variational Continual Learning in Deep Models

Richard E. Turner, Cuong V Nguyen, Yingzhen Li, 2017 .

Variational Bayesian Inference : Algorithms for Sparse Gaussian Processes and Theoretical Bounds

Richard E. Turner, Cuong V Nguyen, Yingzhen Li, 2017 .

A Unifying Approximate Inference Framework from Variational Free Energy Relaxation

Yingzhen Li, 2016 .

Stochastic Expectation Propagation for Large Scale Gaussian Process Classification

Richard E. Turner, José Miguel Hernández-Lobato, Yingzhen Li, 2015, 1511.03249.

Repairing Neural Networks by Leaving the Right Past Behind

Yingzhen Li, Ryutaro Tanno, M. Pradier, 2022, ArXiv.

Learning Neural Set Functions Under the Optimal Subset Oracle

Yatao Bian, Yingzhen Li, P. Zhao, 2022, 2203.01693.

Topics in Approximate Inference

Yingzhen Li, 2017 .

Repairing Neural Networks by Leaving the Right Past Behind

Yingzhen Li, Ryutaro Tanno, M. Pradier, 2022, NeurIPS.

Markovian Gaussian Process Variational Autoencoders

Carles Balsells Rodas, Yingzhen Li, Harrison Zhu, 2022, ArXiv.

Approximate inference: new visions

Yingzhen Li, Yingzhen Li, 2018 .

A Causal View on Robustness of Neural Networks pdf

Kun Zhang, Yingzhen Li, Cheng Zhang, 2020, NeurIPS.

Active Slices for Sliced Stein Discrepancy pdf

Jos'e Miguel Hern'andez-Lobato, José Miguel Hernández-Lobato, Wenbo Gong, 2021, ICML.

Disentangled Sequential Autoencoder

Stephan Mandt, Yingzhen Li, S. Mandt, 2018, ICML.

Deep Gaussian Processes for Regression using Approximate Expectation Propagation

Daniel Hernández-Lobato, Richard E. Turner, Thang D. Bui, 2016, ICML.

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

Nate Kushman, Andrew Fitzgibbon, Yingzhen Li, 2020, ArXiv.

A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging pdf

Ryosuke Saga, Ryutaro Tanno, Yingzhen Li, 2021, MICCAI.

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

Sebastian Tschiatschek, Cheng Zhang, Richard E. Turner, 2019, AABI.

Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck

Sam Devlin, Sebastian Tschiatschek, Katja Hofmann, 2019, NeurIPS.

Variational Implicit Processes

José Miguel Hernández-Lobato, Chao Ma, Yingzhen Li, 2018, ICML.

Black-Box Alpha Divergence Minimization

Daniel Hernández-Lobato, Richard E. Turner, Thang D. Bui, 2015, ICML.

Interpreting diffusion score matching using normalizing flow pdf

Wenbo Gong, Yingzhen Li, Yingzhen Li, 2021, ArXiv.

Learning Set Functions Under the Optimal Subset Oracle via Equivariant Variational Inference

Yatao Bian, Yingzhen Li, P. Zhao, 2022, ArXiv.

Contextual HyperNetworks for Novel Feature Adaptation pdf

Sebastian Tschiatschek, Richard E. Turner, Angus Lamb, 2021, ArXiv.

Meta-Learning Divergences of Variational Inference

Christopher De Sa, Yingzhen Li, Sam Devlin, 2020, 2007.02912.

Meta-Learning for Variational Inference pdf

Sam Devlin, Cheng Zhang, Ruqi Zhang, 2019, ArXiv.

Learning Neural Set Functions Under the Optimal Subset Oracle

Yatao Bian, Yingzhen Li, P. Zhao, 2022, NeurIPS.

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

Cheng Zhang, Angus Lamb, Yingzhen Li, 2020, ArXiv.

Are Generative Classifiers More Robust to Adversarial Attacks?

Yingzhen Li, Yash Sharma, Yingzhen Li, 2018, ICML.

Energy Discrepancies: A Score-Independent Loss for Energy-Based Models

Jen Ning Lim, Yingzhen Li, Zijing Ou, 2023, NeurIPS.

Training Discrete Energy-Based Models with Energy Discrepancy

Yingzhen Li, Zijing Ou, A. Duncan, 2023, 2307.07595.

ESD: Expected Squared Difference as a Tuning-Free Trainable Calibration Measure

Yingzhen Li, G. Kim, Joshua Tian Jin Tee, 2023, ICLR.

'In-Between' Uncertainty in Bayesian Neural Networks pdf

Richard E. Turner, José Miguel Hernández-Lobato, Yingzhen Li, 2019, ArXiv.

Gradient Estimators for Implicit Models

Richard E. Turner, Yingzhen Li, 2017, ICLR.

A Deep Generative Model for Disentangled Representations of Sequential Data pdf

Stephan Mandt, Yingzhen Li, S. Mandt, 2018, ICML 2018.

Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care pdf

Yordan Zaykov, José Miguel Hernández-Lobato, Cheng Zhang, 2019, ArXiv.

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

Yi Zhu, Ehsan Shareghi, Yingzhen Li, 2021, EACL.

Sliced Kernelized Stein Discrepancy pdf

Jos'e Miguel Hern'andez-Lobato, José Miguel Hernández-Lobato, Wenbo Gong, 2020, ICLR.

On the Identifiability of Markov Switching Models

Carles Balsells Rodas, Yingzhen Li, Yixin Wang, 2023, ArXiv.

Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks

Richard E. Turner, Yingzhen Li, David R. Burt, 2019, ArXiv.

Approximate inference: new visions

Yingzhen Li, 2018 .

Scalable Infomin Learning

Yingzhen Li, Adrian Weller, Wei-Der Sun, 2023, NeurIPS.

Sparse Uncertainty Representation in Deep Learning with Inducing Weights pdf

Hippolyt Ritter, Yingzhen Li, Martin Kukla, 2021, NeurIPS.

Variational Inference with Rényi Divergence pdf

Richard E. Turner, Yingzhen Li, Yingzhen Li, 2016, ArXiv.

Approximate Inference with Amortised MCMC pdf

Qiang Liu, Richard E. Turner, Yingzhen Li, 2017, ArXiv.

Meta-Learning for Stochastic Gradient MCMC pdf

José Miguel Hernández-Lobato, Wenbo Gong, Yingzhen Li, 2018, ICLR.

Markovian Gaussian Process Variational Autoencoders

Carles Balsells Rodas, Yingzhen Li, Harrison Zhu, 2022, ICML.

Reinforcement Learning with Efficient Active Feature Acquisition pdf

Sebastian Tschiatschek, Cheng Zhang, Sinno Jialin Pan, 2020, ArXiv.

Black-Box Alpha Divergence Minimization

Richard E. Turner, José Miguel Hernández-Lobato, Mark Rowland, 2016, ICML.

On the Identifiability of Switching Dynamical Systems

Yingzhen Li, Yixin Wang, Carles Balsells-Rodas, 2023, 2305.15925.

Hierarchical Sparse Variational Autoencoder for Text Encoding pdf

Nigel Collier, Victor Prokhorov, Ehsan Shareghi, 2020, ArXiv.

Meta-Learning for Variational Inference

Chris De Sa, Yingzhen Li, Sam Devlin, 2019, ArXiv.