\begin{thebibliography}{60}
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\providecommand{\url}[1]{\texttt{#1}}
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  \providecommand{\doi}[1]{doi: #1}\else
  \providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi

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\newblock Improved algorithms for linear stochastic bandits.
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Ryan~Prescott Adams and David~JC MacKay.
\newblock Bayesian online changepoint detection.
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R{\'e}da Alami, Odalric Maillard, and Raphael F{\'e}raud.
\newblock Restarted bayesian online change-point detector achieves optimal
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Samaneh Aminikhanghahi and Diane~J Cook.
\newblock A survey of methods for time series change point detection.
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Ian Barnett and Jukka-Pekka Onnela.
\newblock Change point detection in correlation networks.
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Sujay Bhatt, Guanhua Fang, and Ping Li.
\newblock Offline change detection under contamination.
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\bibitem[Bubeck(2015)]{bubeck2015convex}
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Yeshwanth Cherapanamjeri, Samuel~B Hopkins, Tarun Kathuria, Prasad Raghavendra,
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\newblock Algorithms for heavy-tailed statistics: Regression, covariance
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Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter Bartlett, and Michael
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\newblock Optimal mean estimation without a variance.
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\newblock Change-point detection in panel data via double cusum statistic.
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Guido Dartmann, Houbing Song, and Anke Schmeink.
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\bibitem[Lai and Xing(2010)]{lai2010sequential}
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\bibitem[Li and Yu(2021)]{li2021adversarially}
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\end{thebibliography}
