Risk-controlled post-processing yields a threshold-structured policy that follows the baseline except where an oracle fallback sharply reduces conditional violation risk, achieving O(log n/n) expected excess risk in i.i.d. settings and exact risk control under exchangeability.
arXiv preprint arXiv:2406.18814 , year=
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Novel methods for valid conformal prediction after data-dependent model selection without additional sample splitting, with finite-sample guarantees and asymptotic optimality under regularity conditions.
Empirical Bayes conformal prediction converts score variability into r-value nonconformity scores that preserve target coverage while reducing inclusion of high-variance false candidates in image classification, CLIP VLMs, and LLMs.
State-dependent conformal prediction with genetic-algorithm state partitioning and branch-merging reachability produces tighter high-confidence perception-error bounds for scalable verification of neurally controlled autonomous systems.
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Risk-Controlled Post-Processing of Decision Policies
Risk-controlled post-processing yields a threshold-structured policy that follows the baseline except where an oracle fallback sharply reduces conditional violation risk, achieving O(log n/n) expected excess risk in i.i.d. settings and exact risk control under exchangeability.
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Conformal prediction after data-dependent model selection
Novel methods for valid conformal prediction after data-dependent model selection without additional sample splitting, with finite-sample guarantees and asymptotic optimality under regularity conditions.
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Empirical Bayes Conformal Prediction for Vision and Language Models
Empirical Bayes conformal prediction converts score variability into r-value nonconformity scores that preserve target coverage while reducing inclusion of high-variance false candidates in image classification, CLIP VLMs, and LLMs.
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Statistical-Symbolic Verification of Perception-Based Autonomous Systems using State-Dependent Conformal Prediction
State-dependent conformal prediction with genetic-algorithm state partitioning and branch-merging reachability produces tighter high-confidence perception-error bounds for scalable verification of neurally controlled autonomous systems.