Zobrazeno 1 - 4
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pro vyhledávání: '"Huang, Xinmeng"'
Large and complex datasets are often collected from several, possibly heterogeneous sources. Collaborative learning methods improve efficiency by leveraging commonalities across datasets while accounting for possible differences among them. Here we s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::56190dd2d1ff93bf52fcf6fd10a895f5
http://arxiv.org/abs/2306.06291
http://arxiv.org/abs/2306.06291
Evaluating the performance of machine learning models under distribution shift is challenging, especially when we only have unlabeled data from the shifted (target) domain, along with labeled data from the original (source) domain. Recent work sugges
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4495f58cc21be6665a3b887da5b1fb28
The prediction accuracy of machine learning methods is steadily increasing, but the calibration of their uncertainty predictions poses a significant challenge. Numerous works focus on obtaining well-calibrated predictive models, but less is known abo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53df87f27fa59d7712878baff6dd6fa5
In modern machine learning, users often have to collaborate to learn the distribution of the data. Communication can be a significant bottleneck. Prior work has studied homogeneous users -- i.e., whose data follow the same discrete distribution -- an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e572e48dfcf9ff4a603394fc172d19c