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Poster
Centroid Approximation for Bootstrap: Improving Particle Quality at Inference
Mao Ye · Qiang Liu
Bootstrap is a principled and powerful frequentist statistical tool for uncertainty quantification. Unfortunately, standard bootstrap methods are computationally intensive due to the need of drawing a large i.i.d. bootstrap sample to approximate the ideal bootstrap distribution; this largely hinders their application in large-scale machine learning, especially deep learning problems. In this work, we propose an efficient method to explicitly \emph{optimize} a small set of high quality ``centroid'' points to better approximate the ideal bootstrap distribution. We achieve this by minimizing a simple objective function that is asymptotically equivalent to the Wasserstein distance to the ideal bootstrap distribution. This allows us to provide an accurate estimation of uncertainty with a small number of bootstrap centroids, outperforming the naive i.i.d. sampling approach. Empirically, we show that our method can boost the performance of bootstrap in a variety of applications.
Author Information
Mao Ye (UT Austin)
Qiang Liu (UT Austin)
Related Events (a corresponding poster, oral, or spotlight)
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2022 Spotlight: Centroid Approximation for Bootstrap: Improving Particle Quality at Inference »
Wed. Jul 20th 03:40 -- 03:45 PM Room Room 301 - 303
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