Timezone: »
Predicting missing values in tabular data, with uncertainty, is an essential task by itself as well as for downstream tasks such as personalized data acquisition. It is not clear whether state-of-the-art deep generative models for these tasks are well equipped to model the complex relationships that may exist between different features, especially when the subset of observed data are treated as a set. In this work we propose new attention-based models for estimating the joint conditional distribution of randomly missing values in mixed-type tabular data. The models improve on the state-of-the-art Partial Variational Autoencoder (Ma et al. 2019) on a range of imputation and information acquisition tasks.
Author Information
Sarah Lewis (Microsoft Research)
Tatiana Matejovicova (DeepMind)
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.
Angus Lamb (Microsoft Research)
Yordan Zaykov (Microsoft Research)
Leading the probabilistic inference engineering team at Microsoft Research, Cambridge.
Miltiadis Allamanis (Microsoft Research)
Cheng Zhang (Microsoft Research, Cambridge)
Related Events (a corresponding poster, oral, or spotlight)
-
2021 : Accurate Imputation and Efficient Data Acquisitionwith Transformer-based VAEs »
Dates n/a. Room
More from the Same Authors
-
2020 : Contextual HyperNetworks for Novel Feature Adaptation »
Angus Lamb -
2022 Poster: Scalable Infomin Learning »
Yanzhi Chen · weihao sun · Yingzhen Li · Adrian Weller -
2022 : Re-Evaluating Chemical Synthesis Planning Algorithms »
Austin Tripp · Krzysztof Maziarz · Sarah Lewis · Guoqing Liu · Marwin Segler -
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 -
2023 : Retro-fallback: retrosynthetic planning in an uncertain world »
Austin Tripp · Krzysztof Maziarz · Sarah Lewis · Marwin Segler · José Miguel Hernández-Lobato -
2023 : Fast protein backbone generation with SE(3) flow matching »
Jason Yim · Andrew Campbell · Yue Kwang Foong · Sarah Lewis · Victor Satorras · Michael Gastegger · Bas Veeling · Jose Jimenez-Luna · Regina Barzilay · Tommi Jaakkola · Frank Noe -
2023 Poster: Perception Test: A Diagnostic Benchmark for Multimodal Video Models »
Viorica Patraucean · Lucas Smaira · Ankush Gupta · Adria Recasens · Larisa Markeeva · Dylan Banarse · Skanda Koppula · joseph heyward · Mateusz Malinowski · Yi Yang · Carl Doersch · Tatiana Matejovicova · Yury Sulsky · Antoine Miech · Alexandre Fréchette · Hanna Klimczak · Raphael Koster · Junlin Zhang · Stephanie Winkler · Yusuf Aytar · Simon Osindero · Dima Damen · Andrew Zisserman · Joao Carreira -
2023 Poster: Energy Discrepancies: A Score-Independent Loss for Energy-Based Models »
Tobias Schröder · Zijing Ou · Jen Lim · Yingzhen Li · Sebastian Vollmer · Andrew Duncan -
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: Repairing Neural Networks by Leaving the Right Past Behind »
Ryutaro Tanno · Melanie F. Pradier · Aditya Nori · Yingzhen Li -
2022 Poster: Simultaneous Missing Value Imputation and Structure Learning with Groups »
Pablo Morales-Alvarez · Wenbo Gong · Angus Lamb · Simon Woodhead · Simon Peyton Jones · Nick Pawlowski · Miltiadis Allamanis · Cheng Zhang -
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 Poster: Sparse Uncertainty Representation in Deep Learning with Inducing Weights »
Hippolyt Ritter · Martin Kukla · Cheng Zhang · Yingzhen Li -
2021 Poster: Self-Supervised Bug Detection and Repair »
Miltiadis Allamanis · Henry Jackson-Flux · Marc Brockschmidt -
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 : Q&A and discussion »
Jack Wang · Angus Lamb -
2020 : Competition overview: motivation, impact, dataset, tasks »
Angus Lamb -
2020 : introduction to the 2020 NeurIPS education challenge »
Angus Lamb -
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 -
2019 Poster: Program Synthesis and Semantic Parsing with Learned Code Idioms »
Richard Shin · Miltiadis Allamanis · Marc Brockschmidt · Oleksandr Polozov -
2018 Poster: Constrained Graph Variational Autoencoders for Molecule Design »
Qi Liu · Miltiadis Allamanis · Marc Brockschmidt · Alexander Gaunt -
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