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PAC learning

Probably Approximately Correct (PAC) learning analyzes machine learning mathematically using probability bounds.

Papers

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Showing 1–25 of 289 papers

TitleStatusHype
VICE: Variational Interpretable Concept EmbeddingsCode1
Prospective Learning: Learning for a Dynamic FutureCode1
Lean Formalization of Generalization Error Bound by Rademacher ComplexityCode1
A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability—0
A Computational Separation between Private Learning and Online Learning—0
Active-learning-based non-intrusive Model Order Reduction—0
A Complete Characterization of Statistical Query Learning with Applications to Evolvability—0
A Distributional-Lifting Theorem for PAC Learning—0
Adversarial Laws of Large Numbers and Optimal Regret in Online Classification—0
Adversarially Robust Learning with Tolerance—0
Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds—0
Adversarial Robustness: What fools you makes you stronger—0
Agnostic Learning by Refuting—0
Agnostic Learning of a Single Neuron with Gradient Descent—0
Agnostic PAC Learning of k-juntas Using L2-Polynomial Regression—0
Agnostic Multi-Group Active Learning—0
Agnostic Smoothed Online Learning—0
AI Reasoning Systems: PAC and Applied Methods—0
A learning problem that is independent of the set theory ZFC axioms—0
Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks—0
Algorithms and SQ Lower Bounds for Robustly Learning Real-valued Multi-index Models—0
A Linear Theory of Multi-Winner Voting—0
An Active Learning Framework for Constructing High-fidelity Mobility Maps—0
Analyzing Robustness of Angluin's L* Algorithm in Presence of Noise—0
Active Learning for Contextual Search with Binary Feedbacks—0
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