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Accurate Imputation and Efficient Data Acquisitionwith Transformer-based VAEs
Sarah Lewis · Tatiana Matejovicova · Yingzhen Li · Angus Lamb · Yordan Zaykov · Miltiadis Allamanis · Cheng Zhang
Event URL: https://openreview.net/forum?id=N_OwBEYTcKK »

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)

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