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PAC learning

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

Papers

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Showing 281–289 of 289 papers

TitleStatusHype
A Complete Characterization of Statistical Query Learning with Applications to Evolvability—0
A Computational Separation between Private Learning and Online Learning—0
Active-learning-based non-intrusive Model Order Reduction—0
Active Learning for Contextual Search with Binary Feedbacks—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
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