Visual Explanations for Convolutional Neural Networks via Input Resampling. - Benjamin J. Lengerich; Sandeep Konam; Eric P. Xing; Stephanie Rosenthal; Manuela M. Veloso (CoRR, 2017)

Retrofitting Distributional Embeddings to Knowledge Graphs with Functional Relations. - Benjamin J. Lengerich; Andrew L. Maas; Christopher Potts (COLING, 2018)

Personalized regression enables sample-specific pan-cancer analysis. - Benjamin J. Lengerich; Bryon Aragam; Eric P. Xing (Bioinform., 2018)

Hybrid Subspace Learning for High-Dimensional Data. - Micol Marchetti-Bowick; Benjamin J. Lengerich; Ankur P. Parikh; Eric P. Xing (CoRR, 2018)

Learning Sample-Specific Models with Low-Rank Personalized Regression. - Benjamin J. Lengerich; Bryon Aragam; Eric P. Xing (NeurIPS, 2019)

Precision Lasso: accounting for correlations and linear dependencies in high-dimensional genomic data. - Haohan Wang; Benjamin J. Lengerich; Bryon Aragam; Eric P. Xing (Bioinform., 2019)

Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models. - Benjamin J. Lengerich; Sarah Tan; Chun-Hao Chang; Giles Hooker; Rich Caruana (AISTATS, 2020)

On Dropout, Overfitting, and Interaction Effects in Deep Neural Networks. - Benjamin J. Lengerich; Eric P. Xing; Rich Caruana (CoRR, 2020)

Data-Driven Patterns in Protective Effects of Ibuprofen and Ketorolac on Hospitalized Covid-19 Patients. - Rich Caruana; Benjamin J. Lengerich; Yindalon Aphinyanaphongs (AMIA, 2021)

How Interpretable and Trustworthy are GAMs? - Chun-Hao Chang; Sarah Tan; Benjamin J. Lengerich; Anna Goldenberg; Rich Caruana (KDD, 2021)

Neural Additive Models: Interpretable Machine Learning with Neural Nets. - Rishabh Agarwal; Levi Melnick; Nicholas Frosst; Xuezhou Zhang; Benjamin J. Lengerich; Rich Caruana; Geoffrey E. Hinton (NeurIPS, 2021)

Ten Quick Tips for Deep Learning in Biology. - Benjamin D. Lee; Anthony Gitter; Casey S. Greene; Sebastian Raschka; Finlay Maguire; Alexander J. Titus; Michael D. Kessler; Alexandra J. Lee; Marc G. Chevrette; Paul Allen Stewart; Thiago Britto-Borges; Evan M. Cofer; et al. (CoRR, 2021)

NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters. - Benjamin J. Lengerich; Caleb Ellington; Bryon Aragam; Eric P. Xing; Manolis Kellis (CoRR, 2021)

Sample-Specific Models for Precision Medicine. - Benjamin J. Lengerich (2021)

Dropout as a Regularizer of Interaction Effects. - Benjamin J. Lengerich; Eric P. Xing; Rich Caruana (AISTATS, 2022)

Executive Function: A Contrastive Value Policy for Resampling and Relabeling Perceptions via Hindsight Summarization? - Chris Lengerich; Benjamin J. Lengerich (CoRR, 2022)

Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes. - Tomas M. Bosschieter; Zifei Xu; Hui Lan; Benjamin J. Lengerich; Harsha Nori; Kristin Sitcov; Vivienne Souter; Rich Caruana (CoRR, 2022)

Estimating Discontinuous Time-Varying Risk Factors and Treatment Benefits for COVID-19 with Interpretable ML. - Benjamin J. Lengerich; Mark E. Nunnally; Yin Aphinyanaphongs; Rich Caruana (CoRR, 2022)

Automated interpretable discovery of heterogeneous treatment effectiveness: A COVID-19 case study. - Benjamin J. Lengerich; Mark E. Nunnally; Yin Aphinyanaphongs; Caleb Ellington; Rich Caruana (J. Biomed. Informatics, 2022)

Ten quick tips for deep learning in biology. - Benjamin D. Lee; Anthony Gitter; Casey S. Greene; Sebastian Raschka; Finlay Maguire; Alexander J. Titus; Michael D. Kessler; Alexandra J. Lee; Marc G. Chevrette; Paul Allen Stewart; Thiago Britto-Borges; Evan M. Cofer; et al. (PLoS Comput. Biol., 2022)

LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs. - Benjamin J. Lengerich; Sebastian Bordt; Harsha Nori; Mark E. Nunnally; Yin Aphinyanaphongs; Manolis Kellis; Rich Caruana (CoRR, 2023)

Contextualized Machine Learning. - Benjamin J. Lengerich; Caleb N. Ellington; Andrea Rubbi; Manolis Kellis; Eric P. Xing (CoRR, 2023)

Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning. - Jannik Deuschel; Caleb Ellington; Yingtao Luo; Benjamin J. Lengerich; Pascal Friederich; Eric P. Xing (ICML, 2024)

Data Science with LLMs and Interpretable Models. - Sebastian Bordt; Benjamin J. Lengerich; Harsha Nori; Rich Caruana (CoRR, 2024)

Patient-Specific Models of Treatment Effects Explain Heterogeneity in Tuberculosis. - Ethan Wu; Caleb Ellington; Benjamin J. Lengerich; Eric P. Xing (CoRR, 2024)

Contextualized: Heterogeneous Modeling Toolbox. - Caleb N. Ellington; Benjamin J. Lengerich; Wesley Lo; Aaron Alvarez; Andrea Rubbi; Manolis Kellis; Eric P. Xing (J. Open Source Softw., 2024)

Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes. - Tomas M. Bosschieter; Zifei Xu; Hui Lan; Benjamin J. Lengerich; Harsha Nori; Ian S. Painter; Vivienne Souter; Rich Caruana (J. Heal. Informatics Res., 2024)