Tag: target
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A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation
A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation arXiv:2507.22632v1 Announce Type: new Abstract: Domain adaptation seeks to leverage the abundant label information in a source domain to improve classification performance in a target domain with limited labels. While the field has seen extensive methodological development, its theoretical foundations remain relatively underexplored.…
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When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts
When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts arXiv:2507.14661v1 Announce Type: new Abstract: Semi-supervised domain adaptation (SSDA) aims to achieve high predictive performance in the target domain with limited labeled target data by exploiting abundant source and unlabeled target data. Despite its significance in numerous…
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On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective
On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective arXiv:2507.06552v1 Announce Type: new Abstract: This paper studies the hardness of unsupervised domain adaptation (UDA) under covariate shift. We model the uncertainty that the learner faces by a distribution $pi$ in the ground-truth triples $(p, q, f)$ — which we call a UDA…
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Why CatBoost Works So Well: The Engineering Behind the Magic
Why CatBoost Works So Well: The Engineering Behind the Magic Gradient boosting is a cornerstone technique for modeling tabular data due to its speed and simplicity. It delivers great results without any fuss. When you look around you’ll see multiple options like LightGBM, XGBoost, etc. Catboost is one such variant. In this post, we will…
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Heterogeneous transfer learning for high dimensional regression with feature mismatch
Heterogeneous transfer learning for high dimensional regression with feature mismatch arXiv:2412.18081v1 Announce Type: new Abstract: We consider the problem of transferring knowledge from a source, or proxy, domain to a new target domain for learning a high-dimensional regression model with possibly different features. Recently, the statistical properties of homogeneous transfer learning have been investigated. However,…