no code implementations • 16 Oct 2023 • Tomas M. Bosschieter, Zifei Xu, Hui Lan, Benjamin J. Lengerich, Harsha Nori, Ian Painter, Vivienne Souter, Rich Caruana
The interpretability of the EBM models reveals surprising insights into the features contributing to risk (e. g. maternal height is the second most important feature for shoulder dystocia) and may have potential for clinical application in the prediction and prevention of serious complications in pregnancy.
no code implementations • 11 Oct 2023 • Jannik Deuschel, Caleb N. Ellington, Benjamin J. Lengerich, Yingtao Luo, Pascal Friederich, Eric P. Xing
Interpretable policy learning seeks to estimate intelligible decision policies from observed actions; however, existing models fall short by forcing a tradeoff between accuracy and interpretability.
1 code implementation • 2 Aug 2023 • Benjamin J. Lengerich, Sebastian Bordt, Harsha Nori, Mark E. Nunnally, Yin Aphinyanaphongs, Manolis Kellis, Rich Caruana
We show that large language models (LLMs) are remarkably good at working with interpretable models that decompose complex outcomes into univariate graph-represented components.
no code implementations • 12 Jul 2022 • Tomas M. Bosschieter, Zifei Xu, Hui Lan, Benjamin J. Lengerich, Harsha Nori, Kristin Sitcov, Vivienne Souter, Rich Caruana
Most pregnancies and births result in a good outcome, but complications are not uncommon and when they do occur, they can be associated with serious implications for mothers and babies.
1 code implementation • 29 May 2021 • 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
In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling.
no code implementations • 5 Aug 2018 • Micol Marchetti-Bowick, Benjamin J. Lengerich, Ankur P. Parikh, Eric P. Xing
One way to achieve this goal is to perform subspace learning to estimate a small set of latent features that capture the majority of the variance in the original data.
1 code implementation • COLING 2018 • Benjamin J. Lengerich, Andrew L. Maas, Christopher Potts
Knowledge graphs are a versatile framework to encode richly structured data relationships, but it can be challenging to combine these graphs with unstructured data.
1 code implementation • 30 Jul 2017 • Benjamin J. Lengerich, Sandeep Konam, Eric P. Xing, Stephanie Rosenthal, Manuela Veloso
The predictive power of neural networks often costs model interpretability.