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.
The 28th International Conference on Artificial Intelligence and Statistics , year=
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.