
SelfBounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PACBayesian CBound
In the PACBayesian literature, the CBound refers to an insightful rela...
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A General Framework for the Derandomization of PACBayesian Bounds
PACBayesian bounds are known to be tight and informative when studying ...
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Implicit Variational Inference: the Parameter and the Predictor Space
Having access to accurate confidence levels along with the predictions a...
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Improved PACBayesian Bounds for Linear Regression
In this paper, we improve the PACBayesian error bound for linear regres...
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PACBayesian Contrastive Unsupervised Representation Learning
Contrastive unsupervised representation learning (CURL) is the stateof...
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Learning LandmarkBased Ensembles with Random Fourier Features and Gradient Boosting
We propose a Gradient Boosting algorithm for learning an ensemble of ker...
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Dichotomize and Generalize: PACBayesian Binary Activated Deep Neural Networks
We present a comprehensive study of multilayer neural networks with bina...
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PseudoBayesian Learning with Kernel Fourier Transform as Prior
We revisit Rahimi and Recht (2007)'s kernel random Fourier features (RFF...
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Multiview Boosting by Controlling the Diversity and the Accuracy of Viewspecific Voters
In this paper we propose a boosting based multiview learning algorithm, ...
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PACBayes and Domain Adaptation
We provide two main contributions in PACBayesian theory for domain adap...
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PACBayesian Analysis for a twostep Hierarchical Multiview Learning Approach
We study a twolevel multiview learning with more than two views under t...
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PACBayesian Theory Meets Bayesian Inference
We exhibit a strong link between frequentist PACBayesian risk bounds an...
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A New PACBayesian Perspective on Domain Adaptation
We study the issue of PACBayesian domain adaptation: We want to learn, ...
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DomainAdversarial Training of Neural Networks
We introduce a new representation learning approach for domain adaptatio...
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Risk Bounds for the Majority Vote: From a PACBayesian Analysis to a Learning Algorithm
We propose an extensive analysis of the behavior of majority votes in bi...
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PACBayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers
In this paper, we provide two main contributions in PACBayesian theory ...
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An Improvement to the Domain Adaptation Bound in a PACBayesian context
This paper provides a theoretical analysis of domain adaptation based on...
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DomainAdversarial Neural Networks
We introduce a new representation learning algorithm suited to the conte...
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PACBayesian Learning and Domain Adaptation
In machine learning, Domain Adaptation (DA) arises when the distribution...
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