第7回HIAS Brown Bag Seminar開催のお知らせ

日時： 2024年9月19日(木) 12:40-13:40

題目 :「Domain adaptation for extra features using optimal transport」

発表者 : 有竹 俊光（社会科学高等研究院　講師）

要旨： “In conventional supervised machine learning, the assumption that the distributions of training and test data are identical underpins the effectiveness of models trained on labeled training data for unseen test data. However, in real-world applications, this assumption often fails, leading to suboptimal performance during the deployment phase. To mitigate this issue, domain adaptation frameworks are employed to bridge the domain gap by minimizing distribution discrepancies between training and test datasets.

This talk will explore domain adaptation methods grounded in optimal transport theory, with a focus on scenarios where additional features are available exclusively during the test phase, while a common set of features is shared across both the training and test phases. The discussion will introduce a method based on bi-directional optimal transport and a novel approach leveraging fused Gromov–Wasserstein optimal transport, which simultaneously addresses both standard and Gromov–Wasserstein optimal transport problems.”
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開催場所： 一橋大学国立キャンパス 　別館（※）　職員集会室（201）
（※）キャンパスマップ⑤の建物です

言語： 英語

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事前登録期日 : 9月18日(水) 13:00
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