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.
citing papers explorer
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Comparing Two Categorical Gini Correlations with Applications to Classification Problems
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.
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ADAPTive Input Training for Many-to-One Pre-Training on Time-Series Classification
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.
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Data Augmentation of Contrastive Learning is Estimating Positive-incentive Noise
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.
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KAN-MLP-Mixer: A comprehensive investigation of the usage of Kolmogorov-Arnold Networks (KANs) for improving IMU-based Human Activity Recognition
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.
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Federated Client Selection under Partial Visibility: A POMDP Approach with Spatio-Temporal Attention
Proposes a POMDP formulation with spatio-temporal attention RL for client selection in federated learning under partial visibility.
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Building a privacy-preserving Federated Recommender system for mobile devices
A two-stage federated recommendation pipeline separates non-sensitive and sensitive data to enable on-device re-ranking while preserving privacy.