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PAC learning

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

Papers

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Showing 81–90 of 289 papers

TitleStatusHype
Error Exponent in Agnostic PAC Learning—0
Exponential Separation between Two Learning Models and Adversarial Robustness—0
Fairness-Aware PAC Learning from Corrupted Data—0
Fast Collaborative Filtering from Implicit Feedback with Provable Guarantees—0
Fast decision tree learning solves hard coding-theoretic problems—0
Faster PAC Learning and Smaller Coresets via Smoothed Analysis—0
Fast Hyperparameter Tuning using Bayesian Optimization with Directional Derivatives—0
Fast Rates for Bandit PAC Multiclass Classification—0
Probably Approximately Correct Federated Learning—0
Active Learning for Contextual Search with Binary Feedbacks—0
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