Machine Learning in Montpellier, Theory & Practice
Machine learning models are frequently used when inputs are missing during training or prediction, potentially leading to increased bias or impractical models without imputing unobserved variables. Imputing missing values is often inadequate and hard to interpret, especially with complex functions. This talk addresses the challenge of predicting with missing data at test time, highlighting the need for interpretable and practical models, crucial in critical sectors like healthcare. In this talk, I present two novel approaches: the Shared Pattern Sparsity Model (SPSM) for scenarios with recurrent missing data patterns, which promotes efficient data use and interpretability without reliance on imputation; and MINTY, a sparse linear rule model, regularized to minimize dependence on features with missing values. This model allows a trade-off between goodness of fit, interpretability, and robustness to missing values at test time. Additionally, I’ll share early results from a project developing a predictive model for sequential risk scores sensitive to missing values across time steps. In collaboration with the Traumabase network, we’ll conduct a user study with clinical professionals to assess the effectiveness of interpretable models in handling missing data.
