Search Results for author:
Found 24 papers, 9 papers with code
Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures
In statistical learning theory, a generalization bound usually involves a complexity measure imposed by the considered theoretical framework.
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound
We investigate a stochastic counterpart of majority votes over finite ensembles of classifiers, and study its generalization properties.
Self-Bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound
In the PAC-Bayesian literature, the C-Bound refers to an insightful relation between the risk of a majority vote classifier (under the zero-one loss) and the first two moments of its margin (i. e., the expected margin and the voters' diversity).
A PAC-Bayes Analysis of Adversarial Robustness
We propose the first general PAC-Bayesian generalization bounds for adversarial robustness, that estimate, at test time, how much a model will be invariant to imperceptible perturbations in the input.
A General Framework for the Practical Disintegration of PAC-Bayesian Bounds
PAC-Bayesian bounds are known to be tight and informative when studying the generalization ability of randomized classifiers.
A survey on domain adaptation theory: learning bounds and theoretical guarantees
Despite a large amount of different transfer learning scenarios, the main objective of this survey is to provide an overview of the state-of-the-art theoretical results in a specific, and arguably the most popular, sub-field of transfer learning, called domain adaptation.
Metric Learning from Imbalanced Data
A key element of any machine learning algorithm is the use of a function that measures the dis/similarity between data points.
Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting
Unlike state-of-the-art Multiple Kernel Learning techniques that make use of a pre-computed dictionary of kernel functions to select from, at each iteration we fit a kernel by approximating it as a weighted sum of Random Fourier Features (RFF) and by optimizing their barycenter.
Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior
We revisit Rahimi and Recht (2007)'s kernel random Fourier features (RFF) method through the lens of the PAC-Bayesian theory.
Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters
Different experiments on three publicly available datasets show the efficiency of the proposed approach with respect to state-of-art models.
Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization
We tackle the issue of classifier combinations when observations have multiple views.
PAC-Bayes and Domain Adaptation
Firstly, we propose an improvement of the previous approach we proposed in Germain et al. (2013), which relies on a novel distribution pseudodistance based on a disagreement averaging, allowing us to derive a new tighter domain adaptation bound for the target risk.
PAC-Bayesian Analysis for a two-step Hierarchical Multiview Learning Approach
We study a two-level multiview learning with more than two views under the PAC-Bayesian framework.
A New PAC-Bayesian Perspective on Domain Adaptation
We study the issue of PAC-Bayesian domain adaptation: We want to learn, from a source domain, a majority vote model dedicated to a target one.
PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers
In this paper, we provide two main contributions in PAC-Bayesian theory for domain adaptation where the objective is to learn, from a source distribution, a well-performing majority vote on a different target distribution.
On Generalizing the C-Bound to the Multiclass and Multi-label Settings
The C-bound, introduced in Lacasse et al., gives a tight upper bound on the risk of a binary majority vote classifier.
An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context
This paper provides a theoretical analysis of domain adaptation based on the PAC-Bayesian theory.
Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks
We show that the usual score function for conditional Markov networks can be written as the expectation over the scores of their spanning trees.
Domain adaptation of weighted majority votes via perturbed variation-based self-labeling
In machine learning, the domain adaptation problem arrives when the test (target) and the train (source) data are generated from different distributions.
On the Generalization of the C-Bound to Structured Output Ensemble Methods
This paper generalizes an important result from the PAC-Bayesian literature for binary classification to the case of ensemble methods for structured outputs.
Majority Vote of Diverse Classifiers for Late Fusion
Our method is based on an order-preserving pairwise loss adapted to ranking that allows us to improve Mean Averaged Precision measure while taking into account the diversity of the voters that we want to fuse.
Proceedings of The 38th Annual Workshop of the Austrian Association for Pattern Recognition (ÖAGM), 2014
The 38th Annual Workshop of the Austrian Association for Pattern Recognition (\"OAGM) will be held at IST Austria, on May 22-23, 2014.
Domain Adaptation of Majority Votes via Perturbed Variation-based Label Transfer
In non-DA supervised setting, a theoretical bound - the C-bound - involves this disagreement and leads to a majority vote learning algorithm: MinCq.
PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification
In this work, we propose a PAC-Bayes bound for the generalization risk of the Gibbs classifier in the multi-class classification framework.
