Presents the first online learning-to-defer algorithm with regret bounds O((n + n_e) T^{2/3}) generally and O((n + n_e) sqrt(T)) under low noise for multiclass classification with varying experts.
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Machine learning: Trends, perspectives, and prospects
4 Pith papers cite this work, alongside 8,110 external citations. Polarity classification is still indexing.
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Neural networks represent densities in a variational extended Thomas-Fermi model, yielding binding energies within 0.5% of prior ETF results and reproducing nuclear pasta phases.
By adopting a negative ontology where the true target does not objectively exist, the paper defines Democratic Supervision and derives the EL-MIATTs framework for ML evaluation and learning with Multiple Inaccurate True Targets.
SONARR now uses generic .NET-type logic instead of Boolean-only facts and supports multi-compute to improve performance on network attack analysis and digital-twin modeling.
citing papers explorer
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Online Learning-to-Defer with Varying Experts
Presents the first online learning-to-defer algorithm with regret bounds O((n + n_e) T^{2/3}) generally and O((n + n_e) sqrt(T)) under low noise for multiclass classification with varying experts.
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Neural-Network-Based Variational Method in Nuclear Density Functional Theory: Application to the Extended Thomas-Fermi Model
Neural networks represent densities in a variational extended Thomas-Fermi model, yielding binding energies within 0.5% of prior ETF results and reproducing nuclear pasta phases.
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Negative Ontology of True Target for Machine Learning: Towards Evaluation and Learning under Democratic Supervision
By adopting a negative ontology where the true target does not objectively exist, the paper defines Democratic Supervision and derives the EL-MIATTs framework for ML evaluation and learning with Multiple Inaccurate True Targets.
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Core Logic and Algorithmic Performance Enhancements for a System Vulnerability Analysis Technique for Complex Mission Critical Systems Implementation
SONARR now uses generic .NET-type logic instead of Boolean-only facts and supports multi-compute to improve performance on network attack analysis and digital-twin modeling.