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High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso
TLDR
The least absolute shrinkage and selection operator (Lasso) allows computationally efficient feature selection based on linear dependency between input features and output values. Expand
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K2-ABC: Approximate Bayesian Computation with Kernel Embeddings
TLDR
We propose a fully nonparametric ABC paradigm which circumvents the need for manually selecting summary statistics for models with intractable likelihoods. Expand
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Interpretable Distribution Features with Maximum Testing Power
TLDR
Two semimetrics on probability distributions are proposed, given as the sum of differences of expectations of analytic functions evaluated at spatial or frequency locations. Expand
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A Linear-Time Kernel Goodness-of-Fit Test
TLDR
We propose a novel adaptive test of goodness-of-fit, with computational cost linear in the number of samples. Expand
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Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning
TLDR
We propose squared-loss mutual information regularization (SMIR) for multi-class probabilistic classification, following the information maximization principle. Expand
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Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)
TLDR
We introduce the Locally Linear Latent Variable Model (LL-LVM), a probabilistic model for non-linear manifold discovery that describes a joint distribution over observations, their manifold coordinates and locally linear maps conditioned on a set of neighbourhood relationships. Expand
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An Adaptive Test of Independence with Analytic Kernel Embeddings
TLDR
A new computationally efficient dependence measure, and an adaptive statistical test of independence, are proposed. Expand
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Large sample analysis of the median heuristic.
In kernel methods, the median heuristic has been widely used as a way of setting the bandwidth of RBF kernels. While its empirical performances make it a safe choice under many circumstances, thereExpand
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Implementing News Article Category Browsing Based on Text Categorization Technique
TLDR
We propose a feature called category browsing to enhance the full-text search function of Thai-language news article search engine. Expand
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Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages
TLDR
We propose an efficient nonparametric strategy for learning a message operator in expectation propagation, which takes as input the set of incoming messages to a factor node, and produces an outgoing message as output. Expand
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