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Found 14 papers, 6 papers with code
A Swiss Army Knife for Minimax Optimal Transport
The Optimal transport (OT) problem and its associated Wasserstein distance have recently become a topic of great interest in the machine learning community.
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.
Mean Oriented Riesz Features for Micro Expression Classification
Micro-expressions are brief and subtle facial expressions that go on and off the face in a fraction of a second.
An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data
In this paper, we address the challenging problem of learning from imbalanced data using a Nearest-Neighbor (NN) algorithm.
Learning Interpretable Shapelets for Time Series Classification through Adversarial Regularization
Times series classification can be successfully tackled by jointly learning a shapelet-based representation of the series in the dataset and classifying the series according to this representation.
End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping
In this work, we present an End-to-End Learned Early Classification of Time Series (ELECTS) model that estimates a classification score and a probability of whether sufficient data has been observed to come to an early and still accurate decision.
IoU is not submodular
This short article aims at demonstrate that the Intersection over Union (or Jaccard index) is not a submodular function.
Residual Conv-Deconv Grid Network for Semantic Segmentation
However, for semantic image segmentation, where the task consists in providing a semantic class to each pixel of an image, feature maps reduction is harmful because it leads to a resolution loss in the output prediction.
Improving Max-Sum through Decimation to Solve Loopy Distributed Constraint Optimization Problems
In the context of solving large distributed constraint optimization problems (DCOP), belief-propagation and approximate inference algorithms are candidates of choice.
L$^3$-SVMs: Landmarks-based Linear Local Support Vectors Machines
For their ability to capture non-linearities in the data and to scale to large training sets, local Support Vector Machines (SVMs) have received a special attention during the past decade.
beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data
During the past few years, the machine learning community has paid attention to developping new methods for learning from weakly labeled data.
Lipschitz Continuity of Mahalanobis Distances and Bilinear Forms
Many theoretical results in the machine learning domain stand only for functions that are Lipschitz continuous.
