Algorithms and Theory for Multiple-Source Adaptation

2018-05-20 · NeurIPS 2018 12 · Judy Hoffman, Mehryar Mohri, Ningshan Zhang

This work includes a number of novel contributions for the multiple-source adaptation problem. We present new normalized solutions with strong theoretical guarantees for the cross-entropy loss and other similar losses. We also provide new guarantees that hold in the case where the conditional probabilities for the source domains are distinct. Moreover, we give new algorithms for determining the distribution-weighted combination solution for the cross-entropy loss and other losses. We report the results of a series of experiments with real-world datasets. We find that our algorithm outperforms competing approaches by producing a single robust model that performs well on any target mixture distribution. Altogether, our theory, algorithms, and empirical results provide a full solution for the multiple-source adaptation problem with very practical benefits.

📄 PDF Abstract BibTeX arXiv:1805.08727

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A Theory of Multiple-Source Adaptation with Limited Target Labeled Data

2020-07-19 · Yishay Mansour, Mehryar Mohri, Jae Ro, Ananda Theertha Suresh 외

We present a theoretical and algorithmic study of the multiple-source domain adaptation problem in the common scenario where the learner has access only to a limited amount of labeled target data, but where the learner h…

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While domain adaptation has been actively researched in recent years, most theoretical results and algorithms focus on the single-source-single-target adaptation setting. Naive application of such algorithms on multiple …

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2016-10-14 · Ievgen Redko, Amaury Habrard, Marc Sebban

Domain adaptation (DA) is an important and emerging field of machine learning that tackles the problem occurring when the distributions of training (source domain) and test (target domain) data are similar but different.…

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Adversarial Multiple Source Domain Adaptation

2018-12-01 · NeurIPS 2018 12 · Han Zhao, Shanghang Zhang, Guanhang Wu, José M. F. Moura 외

While domain adaptation has been actively researched, most algorithms focus on the single-source-single-target adaptation setting. In this paper we propose new generalization bounds and algorithms under both classificati…

Classification Domain Adaptation General Classification Generalization Bounds+3