47 citations · 109 across the 14 of their papers we have counts for
9 papers · 1 filter
Data Science with LLMs and Interpretable Models
Sebastian Bordt, Ben Lengerich, Harsha Nori +1
Recent years have seen important advances in the building of interpretable models, machine learning models that are designed to be easily understood by humans. In this work, we sho…
Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes
Tomas M. Bosschieter, Zifei Xu, Hui Lan +5
Although most pregnancies result in a good outcome, complications are not uncommon and can be associated with serious implications for mothers and babies. Predictive modeling has t…
Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning
Jannik Deuschel, Caleb N. Ellington, Yingtao Luo +3
Interpretable policy learning seeks to estimate intelligible decision policies from observed actions; however, existing models force a tradeoff between accuracy and interpretabilit…
Estimating Discontinuous Time-Varying Risk Factors and Treatment Benefits for COVID-19 with Interpretable ML
Benjamin Lengerich, Mark E. Nunnally, Yin Aphinyanaphongs +1
Treatment protocols, disease understanding, and viral characteristics changed over the course of the COVID-19 pandemic; as a result, the risks associated with patient comorbidities…
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes
Tomas M. Bosschieter, Zifei Xu, Hui Lan +5
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 ba…
Dropout as a Regularizer of Interaction Effects
Benjamin Lengerich, Eric P. Xing, Rich Caruana
We examine Dropout through the perspective of interactions. This view provides a symmetry to explain Dropout: given variables, there are possible sets of va…
How Interpretable and Trustworthy are GAMs?
Chun-Hao Chang, Sarah Tan, Ben Lengerich +2
Generalized additive models (GAMs) have become a leading modelclass for interpretable machine learning. However, there are many algorithms for training GAMs, and these can learn di…
Hybrid Subspace Learning for High-Dimensional Data
Micol Marchetti-Bowick, Benjamin J. Lengerich, Ankur P. Parikh +1
The high-dimensional data setting, in which p >> n, is a challenging statistical paradigm that appears in many real-world problems. In this setting, learning a compact, low-dimensi…
Towards Visual Explanations for Convolutional Neural Networks via Input Resampling
Benjamin J. Lengerich, Sandeep Konam, Eric P. Xing +2
The predictive power of neural networks often costs model interpretability. Several techniques have been developed for explaining model outputs in terms of input features; however,…