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08/15/2023
On regularized Radon-Nikodym differentiation
We discuss the problem of estimating Radon-Nikodym derivatives. This pro...
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07/30/2023
Adaptive learning of density ratios in RKHS
Estimating the ratio of two probability densities from finitely many obs...
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07/21/2023
General regularization in covariate shift adaptation
Sample reweighting is one of the most widely used methods for correcting...
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06/06/2023
A rational conjugate gradient method for linear ill-conditioned problems
We consider linear ill-conditioned operator equations in a Hilbert space...
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05/02/2023
Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation
We study the problem of choosing algorithm hyper-parameters in unsupervi...
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02/09/2023
Domain Generalization by Functional Regression
The problem of domain generalization is to learn, given data from differ...
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06/07/2022
Few-Shot Learning by Dimensionality Reduction in Gradient Space
We introduce SubGD, a novel few-shot learning method which is based on t...
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07/06/2020
On Data Augmentation and Adversarial Risk: An Empirical Analysis
Data augmentation techniques have become standard practice in deep learn...
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05/20/2020
ReLU Code Space: A Basis for Rating Network Quality Besides Accuracy
We propose a new metric space of ReLU activation codes equipped with a t...
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04/22/2020
Moment-Based Domain Adaptation: Learning Bounds and Algorithms
This thesis contributes to the mathematical foundation of domain adaptat...
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02/19/2020
Learning Bounds for Moment-Based Domain Adaptation
Domain adaptation algorithms are designed to minimize the misclassificat...
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10/31/2018
Mixture Density Generative Adversarial Networks
Generative Adversarial Networks have surprising ability for generating s...
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11/16/2017
Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment
A novel approach for unsupervised domain adaptation for neural networks ...
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02/28/2017
