CASA achieves 93.9% slide-level accuracy on Camelyon17-WILDS by adversarially augmenting stains in Macenko space with DKW-calibrated coverage, outperforming baselines including in worst-group accuracy.
The Annals of Mathematical Statistics , volume=
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ACP-UCB1 achieves logarithmic upper-quantile regret in stochastic bandits by combining adaptive conformal quantile estimates with UCB-style optimism.
citing papers explorer
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Physics-Grounded Adversarial Stain Augmentation with Calibrated Coverage Guarantees
CASA achieves 93.9% slide-level accuracy on Camelyon17-WILDS by adversarially augmenting stains in Macenko space with DKW-calibrated coverage, outperforming baselines including in worst-group accuracy.
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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.