11 citations · 14 across the 4 of their papers we have counts for
6 papers
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…
Executive Function: A Contrastive Value Policy for Resampling and Relabeling Perceptions via Hindsight Summarization?
Chris Lengerich, Ben Lengerich
We develop the few-shot continual learning task from first principles and hypothesize an evolutionary motivation and mechanism of action for executive function as a contrastive val…
Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models
Benjamin Lengerich, Sarah Tan, Chun-Hao Chang +2
Models which estimate main effects of individual variables alongside interaction effects have an identifiability challenge: effects can be freely moved between main effects and int…
Learning Sample-Specific Models with Low-Rank Personalized Regression
Benjamin Lengerich, Bryon Aragam, Eric P. Xing
Modern applications of machine learning (ML) deal with increasingly heterogeneous datasets comprised of data collected from overlapping latent subpopulations. As a result, traditio…
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,…