Support Distribution Machines

By Barnab'as P'oczos, Liang Xiong, Danica J. Sutherland, Jeff Schneider. 2012

19 citations recorded by the source corpus.

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Citations in the corpus

Towards a Neural Statistician (2016, methodology): “ultiple-instance learning, for a useful survey see Cheplygina et al. [2]. Typical approaches involve adapting kernel based methods such as support measure machines [24], support distribution machines [26] and multiple-instance-kernels [7]. Set2Seq A highly related work is Vinyals et al. [31] where t”
Learning Theory for Distribution Regression (2014, background): “One might also plug into learning algorithms (based on similarities of distributions) consistent Rényi and Tsallis divergence estimates (Póczos et al., 2011, 2012), but these similarity indices are not kernels, and their consistency in specific learning tasks, similarly to the previous works, is…”
Deep Sets (2017, methodology): “We compare against Support Distribution Machines (SDM) using a RBF kernel (Poczos et al., 2012).”
Two-stage sampled learning theory on distributions (2014, background): “One might also plug into learning algorithms (based on similarities of distributions) consistent Rényi and Tsallis divergence estimates [17, 18], but these similarity indices are not kernels, and their consistency in specific learning tasks, similarly to the previous works, is open.”
Finding and Leveraging Structure in Learning Problems (2012, background): “Recently the papers [142, 152, 153] have considered various supervised learning problems on distributions (i.”
Operator-valued Kernels for Learning from Functional Response Data (2015, background): “There are also other papers that deal with machine learning problems where covariates are probability distributions and discuss their relation with FDA (Poczos et al., 2012, 2013; Oliva et al., 2013).”