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Author Information
Hippolyt Ritter (University College London)
Martin Kukla (University of Cambridge)
Cheng Zhang (Microsoft Research, Cambridge)
Yingzhen Li (Imperial College London)
Yingzhen Li is a senior researcher at Microsoft Research Cambridge. She received her PhD from the University of Cambridge, and previously she has interned at Disney Research. She is passionate about building reliable machine learning systems, and her approach combines both Bayesian statistics and deep learning. Her contributions to the approximate inference field include: (1) algorithmic advances, such as variational inference with different divergences, combining variational inference with MCMC and approximate inference with implicit distributions; (2) applications of approximate inference, such as uncertainty estimation in Bayesian neural networks and algorithms to train deep generative models. She has served as area chairs at NeurIPS/ICML/ICLR/AISTATS on related research topics, and she is a co-organizer of the AABI2020 symposium, a flagship event of approximate inference.
More from the Same Authors
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2021 : Accurate Imputation and Efficient Data Acquisitionwith Transformer-based VAEs »
Sarah Lewis · Tatiana Matejovicova · Yingzhen Li · Angus Lamb · Yordan Zaykov · Miltiadis Allamanis · Cheng Zhang -
2021 : Accurate Imputation and Efficient Data Acquisitionwith Transformer-based VAEs »
Sarah Lewis · Tatiana Matejovicova · Yingzhen Li · Angus Lamb · Yordan Zaykov · Miltiadis Allamanis · Cheng Zhang -
2022 Poster: Scalable Infomin Learning »
Yanzhi Chen · weihao sun · Yingzhen Li · Adrian Weller -
2022 : Deep End-to-end Causal Inference »
Tomas Geffner · Javier Antorán · Adam Foster · Wenbo Gong · Chao Ma · Emre Kiciman · Amit Sharma · Angus Lamb · Martin Kukla · Nick Pawlowski · Miltiadis Allamanis · Cheng Zhang -
2022 : Poster session 1 »
Yingzhen Li -
2022 Workshop: NeurIPS 2022 Workshop on Score-Based Methods »
Yingzhen Li · Yang Song · Valentin De Bortoli · Francois-Xavier Briol · Wenbo Gong · Alexia Jolicoeur-Martineau · Arash Vahdat -
2022 Poster: Black-box coreset variational inference »
Dionysis Manousakas · Hippolyt Ritter · Theofanis Karaletsos -
2022 Poster: Repairing Neural Networks by Leaving the Right Past Behind »
Ryutaro Tanno · Melanie F. Pradier · Aditya Nori · Yingzhen Li -
2022 Poster: Learning Neural Set Functions Under the Optimal Subset Oracle »
Zijing Ou · Tingyang Xu · Qinliang Su · Yingzhen Li · Peilin Zhao · Yatao Bian -
2021 Workshop: Bayesian Deep Learning »
Yarin Gal · Yingzhen Li · Sebastian Farquhar · Christos Louizos · Eric Nalisnick · Andrew Gordon Wilson · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2021 : Evaluating Approximate Inference in Bayesian Deep Learning + Q&A »
Andrew Gordon Wilson · Pavel Izmailov · Matthew Hoffman · Yarin Gal · Yingzhen Li · Melanie F. Pradier · Sharad Vikram · Andrew Foong · Sanae Lotfi · Sebastian Farquhar -
2021 Poster: Identifiable Generative models for Missing Not at Random Data Imputation »
Chao Ma · Cheng Zhang -
2020 Poster: On the Expressiveness of Approximate Inference in Bayesian Neural Networks »
Andrew Foong · David Burt · Yingzhen Li · Richard Turner -
2020 Tutorial: (Track1) Advances in Approximate Inference »
Yingzhen Li · Cheng Zhang -
2018 Poster: Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting »
Hippolyt Ritter · Aleksandar Botev · David Barber -
2017 : Introduction »
Cheng Zhang · Francisco Ruiz · Dustin Tran · James McInerney · Stephan Mandt -
2017 Poster: Perturbative Black Box Variational Inference »
Robert Bamler · Cheng Zhang · Manfred Opper · Stephan Mandt