Proposes an inferential framework to test differences in categorical Gini correlations for predictor importance in classification, establishing asymptotic normality and consistency while accommodating unequal dimensions and dependence.
Anguita, Alessandro Ghio, L
7 Pith papers cite this work. Polarity classification is still indexing.
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ADAPT is a new pre-training paradigm that aligns physical properties of time-series data to allow simultaneous training on 162 diverse classification datasets, achieving new state-of-the-art performance.
Data augmentations in contrastive learning are proved to be point estimates of positive-incentive noise, enabling a new learnable π-noise generator framework for augmentations.
A hybrid KAN-MLP architecture with KAN input embedding and specialized LarctanKAN classification layer yields 5.33% average macro F1 gain over pure-MLP baselines in IMU-based human activity recognition.
Proposes a POMDP formulation with spatio-temporal attention RL for client selection in federated learning under partial visibility.
A two-stage federated recommendation pipeline separates non-sensitive and sensitive data to enable on-device re-ranking while preserving privacy.