Provides theoretical characterizations of detection delay for univariate online robust mean change point detection under Huber contamination and heavy tails, plus a multivariate robust mean testing procedure, with matching lower bounds.
A general methodology for fast online changepoint detection
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Introduces the AR(1)-MSBM for evolving multilayer networks and provides online estimators with minimax-optimal rates and community recovery guarantees under stationarity and non-stationarity via adaptive windowing.
citing papers explorer
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Online change point detection under heavy-tailedness and contamination
Provides theoretical characterizations of detection delay for univariate online robust mean change point detection under Huber contamination and heavy tails, plus a multivariate robust mean testing procedure, with matching lower bounds.
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Online Learning for Autoregressive Multilayer Stochastic Block Models under Stationarity and Non-Stationarity
Introduces the AR(1)-MSBM for evolving multilayer networks and provides online estimators with minimax-optimal rates and community recovery guarantees under stationarity and non-stationarity via adaptive windowing.