Benjamin J. Lengerich

According to our database, Benjamin J. Lengerich authored at least 25 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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., March, 2024

Data Science with LLMs and Interpretable Models.

Sebastian Bordt, Benjamin J. Lengerich, Harsha Nori, Rich Caruana

CoRR, 2024

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 N. Ellington, Benjamin J. Lengerich, Yingtao Luo, Pascal Friederich, Eric P. Xing

CoRR, 2023

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

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, Kun-Hsing Yu, Juan Jose Carmona, Elana J. Fertig, Alexandr A. Kalinin, Brandon Signal, Benjamin J. Lengerich, Timothy J. Triche Jr., Simina M. Boca

PLoS Comput. Biol., 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

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

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

Executive Function: A Contrastive Value Policy for Resampling and Relabeling Perceptions via Hindsight Summarization?

Chris Lengerich, Benjamin J. Lengerich

CoRR, 2022

Dropout as a Regularizer of Interaction Effects.

Benjamin J. Lengerich, Eric P. Xing, Rich Caruana

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Sample-Specific Models for Precision Medicine.

Benjamin J. Lengerich

PhD thesis, 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

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, Kun-Hsing Yu, Juan Jose Carmona, Elana J. Fertig, Alexandr A. Kalinin, Beth Signal, Benjamin J. Lengerich, Timothy J. Triche Jr., Simina M. Boca

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

How Interpretable and Trustworthy are GAMs?

Chun-Hao Chang, Sarah Tan, Benjamin J. Lengerich, Anna Goldenberg, Rich Caruana

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Data-Driven Patterns in Protective Effects of Ibuprofen and Ketorolac on Hospitalized Covid-19 Patients.

Rich Caruana, Benjamin J. Lengerich, Yindalon Aphinyanaphongs

Proceedings of the AMIA 2021, American Medical Informatics Association Annual Symposium, San Diego, CA, USA, October 30, 2021, 2021

2020

On Dropout, Overfitting, and Interaction Effects in Deep Neural Networks.

Benjamin J. Lengerich, Eric P. Xing, Rich Caruana

CoRR, 2020

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

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

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

Learning Sample-Specific Models with Low-Rank Personalized Regression.

Benjamin J. Lengerich, Bryon Aragam, Eric P. Xing

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Hybrid Subspace Learning for High-Dimensional Data.

Micol Marchetti-Bowick, Benjamin J. Lengerich, Ankur P. Parikh, Eric P. Xing

CoRR, 2018

Personalized regression enables sample-specific pan-cancer analysis.

Benjamin J. Lengerich, Bryon Aragam, Eric P. Xing

Bioinform., 2018

Retrofitting Distributional Embeddings to Knowledge Graphs with Functional Relations.

Benjamin J. Lengerich, Andrew L. Maas, Christopher Potts

Proceedings of the 27th International Conference on Computational Linguistics, 2018

2017

Visual Explanations for Convolutional Neural Networks via Input Resampling.

Benjamin J. Lengerich, Sandeep Konam, Eric P. Xing, Stephanie Rosenthal, Manuela M. Veloso

CoRR, 2017