3 citations · 5 across the 2 of their papers we have counts for
6 papers · 1 filter
Interpretable Machine Learning: Moving From Mythos to Diagnostics
Valerie Chen, Jeffrey Li, Joon Sik Kim +2
Despite increasing interest in the field of Interpretable Machine Learning (IML), a significant gap persists between the technical objectives targeted by researchers' methods and t…
A Learning Theoretic Perspective on Local Explainability
Jeffrey Li, Vaishnavh Nagarajan, Gregory Plumb +1
In this paper, we explore connections between interpretable machine learning and learning theory through the lens of local approximation explanations. First, we tackle the traditio…
Explaining Groups of Points in Low-Dimensional Representations
Gregory Plumb, Jonathan Terhorst, Sriram Sankararaman +1
A common workflow in data exploration is to learn a low-dimensional representation of the data, identify groups of points in that representation, and examine the differences betwee…
Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version)
Gregory Plumb, Maruan Al-Shedivat, Eric Xing +1
Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post…
Regularizing Black-box Models for Improved Interpretability
Gregory Plumb, Maruan Al-Shedivat, Angel Alexander Cabrera +3
Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post…
Model Agnostic Supervised Local Explanations
Gregory Plumb, Denali Molitor, Ameet Talwalkar
Model interpretability is an increasingly important component of practical machine learning. Some of the most common forms of interpretability systems are example-based, local, and…