PAC-Bayes framework derives high-probability performance bounds for learned controllers on unknown stochastic linear discrete-time systems and provides efficient algorithms for finite and infinite controller spaces.
Formal Verification and Control with Conformal Prediction
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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.
The work develops an iterative safe planner that adjusts conformal prediction bounds across policy updates via sensitivity analysis to maintain distribution-free safety guarantees despite interaction-induced distribution shifts.
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.
OSCP optimizes conformal prediction score offsets via MILP minimization of an empirical region-size proxy for time-series, with validity guarantees and reduced computation versus prior methods.
Formal connections between PAC bounds for three data-driven reachability methods are established, with empirical results showing they are not interchangeable despite similarities.
citing papers explorer
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A PAC-Bayes Approach for Controlling Unknown Linear Discrete-time Systems
PAC-Bayes framework derives high-probability performance bounds for learned controllers on unknown stochastic linear discrete-time systems and provides efficient algorithms for finite and infinite controller spaces.
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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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Safe Planning in Interactive Environments via Iterative Policy Updates and Adversarially Robust Conformal Prediction
The work develops an iterative safe planner that adjusts conformal prediction bounds across policy updates via sensitivity analysis to maintain distribution-free safety guarantees despite interaction-induced distribution shifts.
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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.
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Efficient Quantification of Time-Series Prediction Error: Optimal Selection Conformal Prediction
OSCP optimizes conformal prediction score offsets via MILP minimization of an empirical region-size proxy for time-series, with validity guarantees and reduced computation versus prior methods.
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Probably Approximately Correct (PAC) Guarantees for Data-Driven Reachability Analysis: A Theoretical and Empirical Comparison
Formal connections between PAC bounds for three data-driven reachability methods are established, with empirical results showing they are not interchangeable despite similarities.