argmax centroidDownload PDF

21 May 2021, 20:47 (modified: 21 Jan 2022, 16:59)NeurIPS 2021 PosterReaders: Everyone
Keywords: distribution approximation, Monte Carlo, approximate inference, multi-domain learning, meta learning, few-shot learning
Abstract: We propose a general method to construct centroid approximation for the distribution of maximum points of a random function (a.k.a. argmax distribution), which finds broad applications in machine learning. Our method optimizes a set of centroid points to compactly approximate the argmax distribution with a simple objective function, without explicitly drawing exact samples from the argmax distribution. Theoretically, the argmax centroid method can be shown to minimize a surrogate of Wasserstein distance between the ground-truth argmax distribution and the centroid approximation under proper conditions. We demonstrate the applicability and effectiveness of our method on a variety of real-world multi-task learning applications, including few-shot image classification, personalized dialogue systems and multi-target domain adaptation.
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