Quentin Rebjock — accepted AI-conference papers

2021

Online false discovery rate control for anomaly detection in time series

Quentin Rebjock, Baris Kurt, Tim Januschowski, Laurent Callot

NeurIPS 2021poster

This article proposes novel rules for false discovery rate control (FDRC) geared towards online anomaly detection in time series. Online FDRC rules allow to control the properties of a sequence of statistical tests. In the context of anomaly detection, the null hypothesis is that an observation is n…

Cited by 18 Source ↗PDF ↗Scholar ↗

2019

Error Feedback Fixes SignSGD and other Gradient Compression Schemes

Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian Stich, Martin Jaggi

ICML 2019oral

Sign-based algorithms (e.g. signSGD) have been proposed as a biased gradient compression technique to alleviate the communication bottleneck in training large neural networks across multiple workers. We show simple convex counter-examples where signSGD does not converge to the optimum. Further, even…

Cited by 616 Source ↗PDF ↗code ↗Scholar ↗