L
Leilani H. Gilpin
Researcher at Massachusetts Institute of Technology
Publications - 27
Citations - 1943
Leilani H. Gilpin is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 7, co-authored 18 publications receiving 976 citations. Previous affiliations of Leilani H. Gilpin include PARC & University of California, Santa Cruz.
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Proceedings ArticleDOI
Explaining Explanations: An Overview of Interpretability of Machine Learning
TL;DR: In an effort to create best practices and identify open challenges, the authors describe foundational concepts of explainability and show how they can be used to classify existing literature, and discuss why current approaches to explanatory methods especially for deep neural networks are insufficient.
Posted Content
Explaining Explanations: An Overview of Interpretability of Machine Learning
TL;DR: In an effort to create best practices and identify open challenges, the authors provide a definition of explainability and show how it can be used to classify existing literature, and discuss why current approaches to explanatory methods especially for deep neural networks are insufficient.
Posted Content
Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning
TL;DR: The definition of explainability is provided and how it can be used to classify existing literature is shown and discussed to create best practices and identify open challenges in explanatory artificial intelligence.
Journal ArticleDOI
Outracing champion Gran Turismo drivers with deep reinforcement learning
Peter R. Wurman,Sam C. Barrett,Kenta Kawamoto,James MacGlashan,Kaushik Subramanian,Tom Walsh,Roberto Capobianco,Alisa Devlic,Franziska Eckert,Florian Fuchs,Leilani H. Gilpin,Piyush Khandelwal,Varun J. Kompella,HaoChih Lin,Patrick MacAlpine,Declan Danesh Oller,Takuma Seno,Craig Sherstan,Mick Thomure,Houmehr Aghabozorgi,Leon Barrett,Rory Douglas,Dion J. Whitehead,Peter Dürr,Peter Stone,Michael Spranger,Hiroaki Kitano +26 more
TL;DR: In this article , the authors describe how they trained agents for Gran Turismo that can compete with the world's best e-sports drivers, and demonstrate the possibilities and challenges of using these techniques to control complex dynamical systems in domains where agents must respect imprecisely defined human norms.
Proceedings Article
Graph analysis for detecting fraud, waste, and abuse in healthcare data
Juan Liu,Eric A. Bier,Aaron Wilson,Tomo Honda,Sricharan Kallur Palli Kumar,Leilani H. Gilpin,John Alexis Guerra-Gomez,Daniel Davies +7 more
TL;DR: A system to detect suspicious activities in large healthcare claims datasets and has been deployed on multiple sites and data sets, both government and commercial, to facilitate the work of FWA investigation analysts.