Public articles linked to the same research event.
arXiv The work replaces one-shot quantile estimation with online feedback control (Adaptive Conformal Inference with tuning-free, locally-adaptive, and group-conditional variants) to address non-exchangeability of candidates during reinforcement-learning search, tracking requested coverage to within about 10^-3 across three architecture families and both single-step and sequential search (three seeds) while pruning 25-50% of evaluations at no measured accuracy cost, whereas static calibration loses coverage control and a Gaussian-process baseline stays conservative.
The work replaces one-shot quantile estimation with online feedback control (Adaptive Conformal Inference with tuning-free, locally-adaptive, and group-conditional variants) to address non-exchangeability of candidates during reinforcement-learning search, tracking requested coverage to within about 10^-3 across three architecture families and both single-step and sequential search (three seeds) while pruning 25-50% of evaluations at no measured accuracy cost, whereas static calibration loses coverage control and a Gaussian-process baseline stays conservative.
The work replaces one-shot quantile estimation with online feedback control (Adaptive Conformal Inference with tuning-free, locally-adaptive, and group-conditional variants) to address non-exchangeability of candidates during reinforcement-learning search, tracking requested coverage to within about 10^-3 across three architecture families and both single-step and sequential search (three seeds) while pruning 25-50% of evaluations at no measured accuracy cost, whereas static calibration loses coverage control and a Gaussian-process baseline stays conservative.
The work replaces one-shot quantile estimation with online feedback control (Adaptive Conformal Inference with tuning-free, locally-adaptive, and group-conditional variants) to address non-exchangeability of candidates during reinforcement-learning search, tracking requested coverage to within about 10^-3 across three architecture families and both single-step and sequential search (three seeds) while pruning 25-50% of evaluations at no measured accuracy cost, whereas static calibration loses coverage control and a Gaussian-process baseline stays conservative.