scientific article; zbMATH DE number 7370519
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Publication:4998861
Aniket Anand Deshmukh, Gilles Blanchard, Ürün Dogan, Clayton Scott, Gyemin Lee
Publication date: 9 July 2021
Full work available at URL: https://arxiv.org/abs/1711.07910
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
kernel methodsRademacher complexityuniversal consistencykernel approximationdomain generalizationgeneralization error bounds
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- Direct importance estimation for covariate shift adaptation
- Classification with asymmetric label noise: consistency and maximal denoising
- Semi-supervised learning of class balance under class-prior change by distribution matching
- On a class of time inhomogeneous nonsingular flows and Schrödinger operators
- Learning from binary labels with instance-dependent noise
- A theory of transfer learning with applications to active learning
- Transfer bounds for linear feature learning
- A theory of learning from different domains
- Adjusting the Outputs of a Classifier to New a Priori Probabilities: A Simple Procedure
- Learning Theory for Distribution Regression
- Hilbert space embeddings and metrics on probability measures
- On the Hardness of Domain Adaptation and the Utility of Unlabeled Target Samples
- Support Vector Machines
- Sample Selection Bias Correction Theory
- Remarks on Inequalities for Large Deviation Probabilities
- Rademacher penalties and structural risk minimization
- Deep Domain Generalization With Structured Low-Rank Constraint
- 10.1162/153244303321897690
- Classification with imperfect training labels
- An Introduction to the Theory of Point Processes
- Convexity, Classification, and Risk Bounds
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