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The paper proposes a robust dual-model collaborative random vector functional link network (KRPRVFL) to improve classification accuracy in the presence of noisy labels and outliers, leveraging kernel risk-sensitive mean p-power criterion and collaborative learning.
This study tests uncertainty sampling in active learning under bounded label noise, comparing error exposure and location effects across datasets to assess robustness and performance.
This paper systematically studies hypergraph node classification under label noise, proposing HyperTrust, a robust framework with HyperedgeBoost and HyperedgePrune modules, along with a unified benchmark for evaluating LLN and GLN methods on hypergraphs.
Introduces LiNC, a lightweight noise correction method that learns per-sample trust parameters to distinguish clean and noisy labels using a Gaussian Mixture Model, achieving robust accuracy gains on medical imaging datasets under high label noise.
This paper reformulates rank estimation with noisy ordinal labels as a stochastic ordering problem and proposes a learning framework (SOL) that captures ordinal label uncertainty through discriminative and stochastic order losses, achieving reliable rank estimation under various noise types.
Introduces CILN, a framework for generating instance-dependent label noise benchmarks through controlled input corruptions, enabling explicit control over ambiguity source and severity. Experiments show it produces realistic noise structures and exposes failure modes in popular noisy-label learning methods.
This paper proposes a novel active learning framework that leverages foundation model priors to jointly address class imbalance and label noise, achieving over 50% annotation savings compared to baselines across image and text domains.