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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Introduces a robust OT divergence with stochastic subgradient algorithm and bootstrap-based SBI procedure for parameter inference under joint geometric and TV contamination.
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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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Robust Simulation Based Inference Through Robust Optimal Transport
Introduces a robust OT divergence with stochastic subgradient algorithm and bootstrap-based SBI procedure for parameter inference under joint geometric and TV contamination.