4 citations · 4 across the 1 of their papers we have counts for
3 papers
cs.LG2019
InterpretML: A Unified Framework for Machine Learning Interpretability
Harsha Nori, Samuel Jenkins, Paul Koch +1
InterpretML is an open-source Python package which exposes machine learning interpretability algorithms to practitioners and researchers. InterpretML exposes two types of interpret…
cs.LG2018
Axiomatic Interpretability for Multiclass Additive Models
Xuezhou Zhang, Sarah Tan, Paul Koch +3
Generalized additive models (GAMs) are favored in many regression and binary classification problems because they are able to fit complex, nonlinear functions while still remaining…
cs.HC2012★ 4 cited
Coordinates: Probabilistic Forecasting of Presence and Availability
Eric J. Horvitz, Paul Koch, Carl Kadie +1
We present methods employed in Coordinate, a prototype service that supports collaboration and communication by learning predictive models that provide forecasts of users s AND ava…