What Interpretable Machine Learning Can Tell Us About Missing Values

Jul 17, 2020

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Missing values are everywhere, and I’ve been dealing with them one way or another for many years. Recently I’ve been doing research in interpretable machine learning. To my surprise, interpretable machine learning has completely changed how I work with missing values. Interpretable learning provides new methods for detecting, understanding, and modeling missing values. In the presentation I’ll show a few surprises where interpretability makes it clear the impact missing values have been having on our machine learning models all along, but which are only visible now thanks to interpretable methods.

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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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