322 citations · 359 across the 5 of their papers we have counts for
6 papers
Training Language Models To Be Coherent Decision-Makers
Khurram Yamin, Xavier Fernandes, Paul Koch +2
Reliable decision-making requires more than accurate prediction: a model must preserve its beliefs, apply the relevant utilities, and recognize when the information needed to justi…
Generating Annotated Training Data for 6D Object Pose Estimation in Operational Environments with Minimal User Interaction
Paul Koch, Marian Schlüter, Serge Thill
Recently developed deep neural networks achieved state-of-the-art results in the subject of 6D object pose estimation for robot manipulation. However, those supervised deep learnin…
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…
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…
Considerations When Learning Additive Explanations for Black-Box Models
Sarah Tan, Giles Hooker, Paul Koch +2
Many methods to explain black-box models, whether local or global, are additive. In this paper, we study global additive explanations for non-additive models, focusing on four expl…
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…