Efficient Missing-value Acquisition with Variational Autoencoders

Jul 17, 2020



In many real-world problems we have to make predictions from feature vectors with missing values. However, we may also be able to observe some of the missing values in the feature vector at a cost. Given the currently observed values, how can we decide which missing values to observe next so that prediction accuracy increases as fast as possible as a function of the observation cost? This problem appears in many different application areas, including medical diagnosis, surveys, recommender systems, insurance, etc. In this talk, I will describe how to solve the problem using an information theoretic approach and novel variational autoencoder models that can effectively deal with missing data.



About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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