StCP leverages transfer learning to stabilize the size of conformal prediction sets without additional target labels.
J.65, 2849–2859 (2022)
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
FedHD is a federated learning framework for whole slide images that distills one-to-one synthetic features aligned via Gaussian mixtures and progressively integrates cross-site features through curriculum learning to handle institutional heterogeneity.
FL with homomorphic encryption matches centralized ML performance for CVD risk prediction but adds cryptographic overhead, while DP-FL has lower cost yet greater accuracy loss especially for logistic regression.
citing papers explorer
-
Stable Localized Conformal Prediction via Transduction
StCP leverages transfer learning to stabilize the size of conformal prediction sets without additional target labels.
-
Federated Distillation for Whole Slide Image via Gaussian-Mixture Feature Alignment and Curriculum Integration
FedHD is a federated learning framework for whole slide images that distills one-to-one synthetic features aligned via Gaussian mixtures and progressively integrates cross-site features through curriculum learning to handle institutional heterogeneity.
-
Privacy-Preserving Federated Learning via Differential Privacy and Homomorphic Encryption for Cardiovascular Disease Risk Modeling
FL with homomorphic encryption matches centralized ML performance for CVD risk prediction but adds cryptographic overhead, while DP-FL has lower cost yet greater accuracy loss especially for logistic regression.