Adapting to online label shift with provable guarantees

Y Bai, YJ Zhang, P Zhao… - Advances in Neural …, 2022 - proceedings.neurips.cc
The standard supervised learning paradigm works effectively when training data shares the
same distribution as the upcoming testing samples. However, this stationary assumption is …
被引用次数：22 相关文章

Adapting to Online Label Shift with Provable Guarantees

Y Bai, YJ Zhang, P Zhao, M Sugiyama… - Advances in Neural …, 2022 - openreview.net
The standard supervised learning paradigm works effectively when training data shares the
same distribution as the upcoming testing samples. However, this stationary assumption is …

Adapting to online label shift with provable guarantees

Y Bai, YJ Zhang, P Zhao, M Sugiyama… - Proceedings of the 36th …, 2022 - dl.acm.org
The standard supervised learning paradigm works effectively when training data shares the
same distribution as the upcoming testing samples. However, this stationary assumption is …

Adapting to Online Label Shift with Provable Guarantees

Y Bai, YJ Zhang, P Zhao, M Sugiyama… - arXiv e …, 2022 - ui.adsabs.harvard.edu
The standard supervised learning paradigm works effectively when training data shares the
same distribution as the upcoming testing samples. However, this stationary assumption is …
[PDF] nju.edu.cn

[PDF][PDF] Adapting to Online Label Shift with Provable Guarantees

Y Bai, YJ Zhang, P Zhao, M Sugiyama… - arXiv preprint arXiv …, 2022 - lamda.nju.edu.cn
The standard supervised learning paradigm works effectively when training data shares the
same distribution as the upcoming testing samples. However, this assumption is often …
[PDF] neurips.cc

[PDF][PDF] Adapting to Online Label Shift with Provable Guarantees

Y Bai, YJ Zhang, P Zhao, M Sugiyama, ZH Zhou - proceedings.neurips.cc
The standard supervised learning paradigm works effectively when training data shares the
same distribution as the upcoming testing samples. However, this stationary assumption is …
[PDF] github.io

[PDF][PDF] Adapting to Online Label Shift with Provable Guarantees

Y Bai, YJ Zhang, P Zhao, M Sugiyama, ZH Zhou - yujie-zhang96.github.io
The standard supervised learning paradigm works effectively when training data shares the
same distribution as the upcoming testing samples. However, this stationary assumption is …

Adapting to Online Label Shift with Provable Guarantees

Y Bai, YJ Zhang, P Zhao, M Sugiyama… - arXiv preprint arXiv …, 2022 - arxiv.org
The standard supervised learning paradigm works effectively when training data shares the
same distribution as the upcoming testing samples. However, this stationary assumption is …
[PDF] nju.edu.cn

[PDF][PDF] Adapting to Online Label Shift with Provable Guarantees

Y Bai, YJ Zhang, P Zhao, M Sugiyama, ZH Zhou - lamda.nju.edu.cn
The standard supervised learning paradigm works effectively when training data shares the
same distribution as the upcoming testing samples. However, this stationary assumption is …