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Precision Lasso: accounting for correlations and linear dependencies in high-dimensional genomic data., , , and . Bioinform., 35 (7): 1181-1187 (2019)Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models., , , , and . CoRR, (2019)Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning., , , , , and . CoRR, (2023)Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes., , , , , , , and . J. Heal. Informatics Res., 8 (1): 65-87 (March 2024)NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters., , , , and . CoRR, (2021)Hybrid Subspace Learning for High-Dimensional Data., , , and . CoRR, (2018)Personalized regression enables sample-specific pan-cancer analysis., , and . Bioinform., 34 (13): i178-i186 (2018)Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes., , , , , , , and . CoRR, (2022)Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models., , , , and . AISTATS, volume 108 of Proceedings of Machine Learning Research, page 2402-2412. PMLR, (2020)How Interpretable and Trustworthy are GAMs?, , , , and . CoRR, (2020)