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Activity Number: 23 - Recents Advances in Statistical Learning and Network Data Analysis
Type: Topic Contributed
Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #329592 Presentation
Title: High-dimensional Cost-constrained Regression via Non-convex Optimization
Author(s): Yufeng Liu*
Companies: University of North Carolina at Chapel Hill
Keywords: Individual Treatment Rule; Optimal Decisons; Personalized Medicine; Supervised Learning; Value Functions

In modern predictive modeling process, budget constraints become a very important consideration due to the high cost of collecting data. This motivates us to develop new and efficient high-dimensional cost constrained predictive modeling methods. In this talk, to address this challenge, we first study a new non-convex high-dimensional cost-constrained linear regression problem. The non-convex budget constraint makes this problem NP-hard. In order to estimate the regression coefficient vector of the cost-constrained regression model, we propose a new discrete extension of recent first-order continuous optimization methods. We further show some extensions of our proposed method. Both theoretical and numerical studies are used to demonstrate the effectiveness of the proposed method.

Authors who are presenting talks have a * after their name.

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