Open AccessProceedings Article
Generalizing from Several Related Classification Tasks to a New Unlabeled Sample
Gilles Blanchard,Gyemin Lee,Clayton Scott +2 more
- Vol. 24, pp 2178-2186
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TLDR
This work develops a distribution-free, kernel-based approach to the problem of assigning class labels to an unlabeled test data set, and presents generalization error analysis, describe universal kernels, and establish universal consistency of the proposed methodology.Citations
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WILDS: A Benchmark of in-the-Wild Distribution Shifts
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In Search of Lost Domain Generalization
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References
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Support Vector Machines
TL;DR: This book explains the principles that make support vector machines (SVMs) a successful modelling and prediction tool for a variety of applications and provides a unique in-depth treatment of both fundamental and recent material on SVMs that so far has been scattered in the literature.
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TL;DR: In this article, the authors discuss the relationship between Markov Processes and Ergodic properties of Markov processes and their relation with PDEs and potential theory. But their main focus is on the convergence of random processes, measures, and sets.
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Making large scale SVM learning practical
TL;DR: SVM light as discussed by the authors is an implementation of an SVM learner which addresses the problem of large-scale SVM training with many training examples on the shelf, which makes large scale SVM learning more practical.
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Multitask learning
TL;DR: Multitask learning as discussed by the authors is an approach to inductive transfer that improves learning for one task by using the information contained in the training signals of other related tasks, and it does this by learning tasks in parallel while using a shared representation; what is learned for each task can help other tasks be learned better.
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Rademacher and gaussian complexities: risk bounds and structural results
TL;DR: In this paper, the authors investigate the use of data-dependent estimates of the complexity of a function class, called Rademacher and Gaussian complexities, in a decision theoretic setting and prove general risk bounds in terms of these complexities.