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James Fogarty

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

AAAI Conference 2011 Conference Paper

Effective End-User Interaction with Machine Learning

Saleema Amershi
James Fogarty
Ashish Kapoor
Desney Tan

End-user interactive machine learning is a promising tool for enhancing human productivity and capabilities with large unstructured data sets. Recent work has shown that we can create end-user interactive machine learning systems for specific applications. However, we still lack a generalized understanding of how to design effective end-user interaction with interactive machine learning systems. This work presents three explorations in designing for effective end-user interaction with machine learning in CueFlik, a system developed to support Web image search. These explorations demonstrate that interactions designed to balance the needs of end-users and machine learning algorithms can significantly improve the effectiveness of end-user interactive machine learning.

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IJCAI Conference 2011 Conference Paper

Using Multiple Models to Understand Data

Kayur Patel
Steven M. Drucker
James Fogarty
Ashish Kapoor
Desney S. Tan

A human's ability to diagnose errors, gather data, and generate features in order to build better models is largely untapped. We hypothesize that analyzing results from multiple models can help people diagnose errors by understanding relationships among data, features, and algorithms. These relationships might otherwise be masked by the bias inherent to any individual model. We demonstrate this approach in our Prospect system, show how multiple models can be used to detect label noise and aid in generating new features, and validate our methods in a pair of experiments.

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AAAI Conference 2008 Conference Paper

Intelligence in Wikipedia

Daniel S. Weld
Eytan Adar
James Fogarty
Kayur Patel

The Intelligence in Wikipedia project at the University of Washington is combining self-supervised information extraction (IE) techniques with a mixed initiative interface designed to encourage communal content creation (CCC). Since IE and CCC are each powerful ways to produce large amounts of structured information, they have been studied extensively — but only in isolation. By combining the two methods in a virtuous feedback cycle, we aim for substantial synergy. While previous papers have described the details of individual aspects of our endeavor [25, 26, 24, 13], this report provides an overview of the project’s progress and vision.

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