ACP-UCB1 achieves logarithmic upper-quantile regret in stochastic bandits by combining adaptive conformal quantile estimates with UCB-style optimism.
Angelopoulos and Stephen Bates
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Conformal prediction coverage collapses before accuracy during lifelong LLM fine-tuning, and a lightweight calibration replay using small task buffers can restore nominal coverage.
citing papers explorer
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Conformal-Style Quantile Analyses for Stochastic Bandits
ACP-UCB1 achieves logarithmic upper-quantile regret in stochastic bandits by combining adaptive conformal quantile estimates with UCB-style optimism.
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Continual Calibration: Coverage Can Collapse Before Accuracy in Lifelong LLM Fine-Tuning
Conformal prediction coverage collapses before accuracy during lifelong LLM fine-tuning, and a lightweight calibration replay using small task buffers can restore nominal coverage.