Two-stage sampled learning theory on distributions

By Zoltán Szabó, Arthur Gretton, Barnab'as P'oczos, Bharath K. Sriperumbudur. 2014

85 citations recorded by the source corpus.

Subfields: Statistics and Probability, Statistical Learning Theory

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Citations in the corpus

Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, methodology): “A slightly different interpretation is to look at this problem as a learning problem on probability distributions (Muandet et al. 2012, Muandet and Schölkopf 2013, Szabó et al. 2015).”
Prototypal Analysis and Prototypal Regression (2017, background): “Szabó et al. (2015, 2016) performs a similar extension for kernel ridge regression.”
Minimax Estimation of Kernel Mean Embeddings (2016, background): “…et al., 2008; Fukumizu et al., 2008), covariate-shift (Smola et al., 2007), density estimation (Sriperumbudur, 2011), feature selection (Song et al., 2012), causal inference (Lopez-Paz et al., 2015), kernel Bayes’ rule (Fukumizu et al., 2013) and distribution regression (Szabó et al., 2015).”
Computing functions of random variables via reproducing kernel Hilbert space representations (2015, neutral): “…successfully in many domains such as graphical models (Song et al. 2010, 2011), two-sample testing (Gretton et al. 2012), domain adaptation (Huang et al. 2007; Gretton et al. 2009; Muandet et al. 2013), and supervised learning on probability distributions (Muandet et al. 2012; Szabó et al. 2014).”
Who Supported Obama in 2012?: Ecological Inference through Distribution Regression (2015, methodology): “…to estimate: Next, use kernel ridge regression [29] to learn a function f : where the objective is to minimize the L 2 loss subject to a “ridge” complexity penalty weighted by a positive constant λ : In [33] a variety of kernels for f corresponding to the Hilbert space H f are considered.”
The Randomized Causation Coefficient (2014, methodology): “Finally, the adaption of the distributional learning theory of Szabó et al. (2014) to analyze our randomized, classification setting.”
From Dependence to Causation (2016, background): “For that, it suffices to decompose g(x, y) = (g1(x, y), g2(x, y)) and show that g1 and g2 are measurable, as done by Szabó et al. (2014).”
Domain Generalization by Marginal Transfer Learning (2017, background): “Learning Theoretic Study Although the regularized estimation formula (3) defining f̂λ is standard, the generalization error analysis is not, since the X̃ij are neither identically distributed nor independent (Szabo et al., 2015).”