Learning with Missing Values

Julie Josse · Jes Frellsen · Pierre-Alexandre Mattei · Gael Varoquaux

Keywords:  Graphical Models    Missing values    Matrix Completion    Record Linkage    Selection Bias  


Analysis of large amounts of data offers new opportunities to understand many processes better. Yet, data accumulation often implies relaxing acquisition procedures or compounding diverse sources, leading to many observations with missing features. From questionnaires to collaborative filtering, from electronic health records to single-cell analysis, missingness is everywhere at play and is rather the norm than the exception. Even “clean” data sets are often barely “cleaned” versions of incomplete data sets—with all the unfortunate biases this cleaning process may have created.

Despite this ubiquity, tackling missing values is often overlooked. Handling missing values poses many challenges, and there is a vast literature in the statistical community, with many implementations available. Yet, there are still many open issues and the need to design new methods or to introduce new point of views: for missing values in a supervised-learning setting, in deep learning architectures, to adapt available methods for high dimensional observed data with different type of missing values, deal with feature mismatch and distribution mismatch. Missing data is one of the eight pillars of causal wisdom for Judea Pearl who brought graphical model reasoning to tackle some missing not at random values.

To the best of our knowledge, this is the first workshop at the major machine learning conferences focusing primarily on missing value problems in recent years. The goal of our workshop is to give more momentum and exposition to research on missing values, both theoretical and methodological, and emphasize the connections with other areas of machine learning (e.g. causal inference, generative modelling, uncertainty quantification, transfer learning, distributional shift, etc.). We will also attach importance to discussing the reproducibility problems that can be caused by missing data, the danger of forgetting the missing values issues and the importance of providing sound implementations.

We welcome both academic and industrial practitioners/researchers. In particular, since missing data is a critical issue in many applications, we would like to federate industrial/applied know-how and various academic approaches.

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Timezone: America/Los_Angeles »