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This paper presents a certification-inspired mechanism for automatic speech recognition that uses a dual-gate diagnostic pipeline (Two-Sided Atomic Audit and Rank-Based Tournament) to provide certified robustness and achieve up to a 55% relative reduction in word error rate across diverse architectures.
This paper introduces a meta-learning framework for anytime-valid certified robustness that uses sequential E-processes to adaptively allocate compute, achieving a 20-fold reduction in sample complexity compared to traditional randomized smoothing while maintaining rigorous statistical guarantees.
RRISE introduces a learned surrogate estimator that reduces the Monte Carlo sampling cost of randomized smoothing for certified robustness to a single forward pass, maintaining accuracy within 0.84 percentage points while replacing up to 10^4 evaluations per query.