Improving online FDR procedures via online analogs of e-closure and compound e-values

Ziyu Xu, Lasse Fischer, Aaditya Ramdas
Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence, PMLR 337:7552-7561, 2026.

Abstract

In many scientific applications, hypotheses are generated and tested continuously in a stream. We develop a framework for improving online multiple testing procedures with false discovery rate ({FDR}) control under arbitrary dependence. Our approach is two-fold: we construct methods via the online e-closure principle, as well as a novel formulation in online compound e-values constructed via donations. This yields strict power improvements over state-of-the-art e-value and p-value procedures while retaining {FDR} control. We further derive algorithms that compute the rejection decision at time $t$ in $O(\log t)$ time, and we demonstrate improved empirical performance on synthetic and real data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v337-xu26a, title = {Improving online {FDR} procedures via online analogs of e-closure and compound e-values}, author = {Xu, Ziyu and Fischer, Lasse and Ramdas, Aaditya}, booktitle = {Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence}, pages = {7552--7561}, year = {2026}, editor = {Perković, Emilija and Malinsky, Daniel}, volume = {337}, series = {Proceedings of Machine Learning Research}, month = {17--21 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v337/main/assets/xu26a/xu26a.pdf}, url = {https://proceedings.mlr.press/v337/xu26a.html}, abstract = {In many scientific applications, hypotheses are generated and tested continuously in a stream. We develop a framework for improving online multiple testing procedures with false discovery rate ({FDR}) control under arbitrary dependence. Our approach is two-fold: we construct methods via the online e-closure principle, as well as a novel formulation in online compound e-values constructed via donations. This yields strict power improvements over state-of-the-art e-value and p-value procedures while retaining {FDR} control. We further derive algorithms that compute the rejection decision at time $t$ in $O(\log t)$ time, and we demonstrate improved empirical performance on synthetic and real data.} }
Endnote
%0 Conference Paper %T Improving online FDR procedures via online analogs of e-closure and compound e-values %A Ziyu Xu %A Lasse Fischer %A Aaditya Ramdas %B Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2026 %E Emilija Perković %E Daniel Malinsky %F pmlr-v337-xu26a %I PMLR %P 7552--7561 %U https://proceedings.mlr.press/v337/xu26a.html %V 337 %X In many scientific applications, hypotheses are generated and tested continuously in a stream. We develop a framework for improving online multiple testing procedures with false discovery rate ({FDR}) control under arbitrary dependence. Our approach is two-fold: we construct methods via the online e-closure principle, as well as a novel formulation in online compound e-values constructed via donations. This yields strict power improvements over state-of-the-art e-value and p-value procedures while retaining {FDR} control. We further derive algorithms that compute the rejection decision at time $t$ in $O(\log t)$ time, and we demonstrate improved empirical performance on synthetic and real data.
APA
Xu, Z., Fischer, L. & Ramdas, A.. (2026). Improving online FDR procedures via online analogs of e-closure and compound e-values. Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 337:7552-7561 Available from https://proceedings.mlr.press/v337/xu26a.html.

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