Computationally Efficient Feature Significance and Importance for Machine Learning Models
Kay Giesecke
Founder, Chairman and Chief Scientist, Professor at Stanford University
Enguerrand Horel
Senior Research Scientist, Upstart
We develop a simple and computationally efficient significance test for the features of a machine learning model. Our forward-selection approach applies to any model specification, learning task and variable type. The test is non-asymptotic, straightforward to implement, and does not require model refitting. It identifies the statistically significant features as well as feature interactions of any order in a hierarchical manner, and generates a model-free notion of feature importance. Experimental and empirical results illustrate its performance.
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About the Speaker
Kay Giesecke
Founder, Chairman and Chief Scientist, Professor at Stanford University
Kay Giesecke is the Founder, Chairman and Chief Scientist at Infima. He is also Professor of Management Science & Engineering at Stanford University, the director of the Advanced Financial Technologies Laboratory, and the director of the Mathematical and Computational Finance Program. Kay serves on the Governing Board and Scientific Advisory Board of the Consortium for Data Analytics in Risk. He is a member of the Council of the Bachelier Finance Society.
Kay is a financial technologist interested in solving the challenging modeling, statistical, and computational problems arising in fixed-income and credit markets. Together with his students at Stanford, Kay has pioneered the core elements of the deep learning and computational technologies underpinning Infima’s solutions.
Kay’s research has won several awards, including the JP Morgan AI Faculty Research Award (2019) and the Fama/DFA Prize (2011), and has been funded by the National Science Foundation, JP Morgan, State Street, Morgan Stanley, Swiss Re, American Express, Moody's,and several other organizations.
Kay has advised several financial technology startups and has been a consultant to banks,investment and risk management firms, governmental agencies, and supranational organizations.
Enguerrand Horel
Senior Research Scientist, Upstart
He obtained his PhD in Computational and Mathematical Engineering at Stanford University, where he developed and analyzed rigorous statistical approaches to explaining the behavior of machine learning models, especially deep learning. During his doctoral studies he worked in the AI Research teams at JP Morgan and Apple.
