PAC-Bayesian Bounds for Linear Loss
Combining PAC-Bayesian and Generic Chaining Bounds
... a loss function L taking its values [0; 1], the variance of a regret function f can be bounded successively by P f 2 and P f ...localized bounds will not yield any significant ...
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Aggregation by exponential weighting, sharp PAC-Bayesian bounds and sparsity
... squared loss in the model of re- gression with deterministic ...sharp PAC-Bayesian risk bounds for aggre- gates defined via exponential weights, under general assumptions on the distribution ...
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Aggregation by exponential weighting, sharp PAC-Bayesian bounds and sparsity
... In Section 7 we propose a new approach to sparse recovery that realizes a com- promise between the theoretical properties and the computational efficiency. We first suggest a general technique of deriving SOI from the ...
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PAC-Bayesian Generalization Error Bounds for Gaussian Process Classification
... art bounds for the popular support vector machine ...ε-insensitive loss which cannot be seen as the negative log of a proper noise distribution (see Seeger, ...the loss function encourages sparse ...
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PAC-Bayesian Bounds for Sparse Regression Estimation with Exponential Weights
... the PAC-Bayesian techniques of Catoni [16] to build an estimator satisfying a sparsity oracle inequal- ity for the true excess ...putational Bayesian theory, see the monograph of Marin and Robert ...
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PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers
... the PAC-Bayesian framework to tackle domain adap- tation in a binary classification situation without target labels (sometimes called unsupervised domain ...H, PAC-Bayesian theory (introduced ...
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A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers
... the PAC- Bayesian framework to tackle DA in a binary classifica- tion situation without target labels (sometimes called unsupervised domain ...H, PAC-Bayesian theory (introduced by McAllester ...
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Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm
... presented PAC-Bayesian bounds that have the uncommon property of having no Kullback- Leibler divergence term (PAC-Bounds 3 and ...These bounds, together with the C -bound, gave ...
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PAC-Bayesian Inequalities for Martingales
... PAC-Bayesian Inequalities for Martingales Yevgeny Seldin, François Laviolette, Nicolò Cesa-Bianchi, John Shawe-Taylor, and Peter Auer Abstract—We present a set of high-probability inequalities that control ...
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Near-optimal PAC bounds for discounted MDPs
... . Before the proofs, we briefly compare Thereom 3 with the more recent work on the sample complexity of rein- forcement learning when a generative model is available [AMK12]. In that paper they obtain a bound equal (up ...
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PAC-Bayesian Learning and Domain Adaptation
... learned linear classifier. PAC-Bayesian Learning of Adapted Linear Classifier DA Bound for the Gibbs ...combine PAC- Bayesian and DA ...
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A New PAC-Bayesian Perspective on Domain Adaptation
... provide PAC- Bayesian generalization guarantees to justify the empirical minimization of our new domain adaptation bound, and specialize it to linear classifiers (following a methodology known to ...
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PAC-Bayesian high dimensional bipartite ranking
... our PAC-Bayesian estimation strategy for the bipartite ranking problem in Section ...Risk bounds are presented to assess the merits of our procedure and exhibit explicit non-parametric rates of ...of ...
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PAC-Bayesian Analysis of Co-clustering and Beyond
... of PAC-Bayesian analysis lies in its ability to provide a non-uniform treatment of the hypotheses within a hypothesis class, its advantage over traditional PAC analysis is best seen in the analysis ...
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PAC-Bayesian Majority Vote for Late Classifier Fusion
... A lot of attention has been devoted to multimedia indexing over the past few years. In the literature, we often consider two kinds of fusion schemes: The early fusion and the late fusion. In this paper we focus on late ...
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PAC Bayesian inequalities of some random variables sequences
... several PAC-Bayesian inequalities. The PAC-Bayesian analysis is an abbreviation for the Probably Approximately Correct learning model and has been introduced a decade ago (Shawe-Taylor and ...
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A PAC Bayesian Approach to Minimum Perplexity Language Modeling
... present PAC-Bayesian theory as a powerful tool for deriving high probability guarantees as well as efficient and well-motivated ...useful PAC-Bayesian ...the PAC-Bayesian ...
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On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization
... of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization Abstract This work characterizes the generalization ability of algorithms whose predictions are linear in the input ...
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Bounds for Linear Multi-Task Learning
... usual PAC guarantees (but see Ben-David, 2003, for bounds on the individual ...which bounds the estimation error, now exhibits the advantage of multi-task learn- ing: Sharing the preprocessor implies ...
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Generalization Error Bounds for Bayesian Mixture Algorithms
... the PAC-Bayesian framework of McAllester (1999, 2003) to a rather unified setting for Bayesian mixture methods, where different regularization criteria may be incorporated, and their effect on the ...
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