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ORIE Colloquium: Hongseok Namkoong (Stanford) - Learning Models with Uniform Subpopulation Performance

Friday, February 22, 2019 at 3:30pm

Frank H. T. Rhodes Hall, 253

Spurred by recent advances, machine learning methods are beginning to prescribe decisions in high-stakes domains, including hiring and medical diagnoses. The standard machine learning paradigm that optimizes average-case performance, however, often yields models that perform poorly on tail subpopulations, such as underrepresented demographic groups. We present methods that optimize the worst-case subpopulation performance, thereby guaranteeing a uniform level of performance over all subpopulations. Our procedures are convex and computationally efficient. We prove finite-sample minimax upper and lower bounds, showing that uniform subpopulation performance comes at a cost in convergence rates. Empirically, our procedure improves performance on tail subpopulations, and provides a uniform level of performance through time by controlling latent minority proportions.

Hongseok Namkoong is a Ph.D. candidate in the Department of Management Science & Engineering at Stanford University, where he is jointly advised by John Duchi and Peter Glynn. His research focuses on developing robust and reliable machine learning methods that can maintain a uniform level of performance under operation. He received his M.S. in statistics from Stanford, and B.S. in industrial engineering and mathematics from KAIST. Hong is a recipient of a number of awards and fellowships, including best paper awards at the Neural Information Processing Systems conference and the International Conference on Machine Learning (runner-up), and the best student paper award from the INFORMS Applied Probability Society.

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College of Engineering, Operations Research and Information Engineering, Engineering


engineering, operations research, orie, College of Engineering





Hongseok Namkoong

Speaker Affiliation

Department of Management Science & Engineering, Stanford University

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