An adaptive bandit algorithm for multiple change-point localization achieves non-asymptotic sample bounds jointly controlled by jump magnitudes and change-point spacing for any fixed δ and η.
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2 Pith papers cite this work. Polarity classification is still indexing.
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A marginalized transition model with Markov dependence and category-specific changepoint specification is developed for detecting shifts in serially correlated categorical time series, demonstrated on Canadian cloud cover observations.
citing papers explorer
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The Sample Complexity of Multiple Change Point Identification under Bandit Feedback
An adaptive bandit algorithm for multiple change-point localization achieves non-asymptotic sample bounds jointly controlled by jump magnitudes and change-point spacing for any fixed δ and η.
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Changepoint Detection in Categorical Time Series with Application to Daily Total Cloud Cover in Canada
A marginalized transition model with Markov dependence and category-specific changepoint specification is developed for detecting shifts in serially correlated categorical time series, demonstrated on Canadian cloud cover observations.