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PAC learning

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

Papers

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Showing 226–250 of 289 papers

TitleStatusHype
Private learning implies quantum stability—0
Private PAC learning implies finite Littlestone dimension—0
Private PAC Learning May be Harder than Online Learning—0
On Proper Learnability between Average- and Worst-case Robustness—0
Probably Approximately Correct Constrained Learning—0
Probably approximately correct high-dimensional causal effect estimation given a valid adjustment set—0
Probably Approximately Precision and Recall Learning—0
Proper Learning, Helly Number, and an Optimal SVM Bound—0
Proper vs Improper Quantum PAC learning—0
Provable learning of quantum states with graphical models—0
Quantum hardness of learning shallow classical circuits—0
Quantum statistical query learning—0
Query-driven PAC-Learning for Reasoning—0
Ramsey Theorems for Trees and a General 'Private Learning Implies Online Learning' Theorem—0
Realizable Learning is All You Need—0
Reducing Adversarially Robust Learning to Non-Robust PAC Learning—0
Reliable Learning of Halfspaces under Gaussian Marginals—0
Representation, Approximation and Learning of Submodular Functions Using Low-rank Decision Trees—0
Revisiting Agnostic PAC Learning—0
Robust learning under clean-label attack—0
Sample Complexity Bounds for Robustly Learning Decision Lists against Evasion Attacks—0
Sample Complexity Bounds on Differentially Private Learning via Communication Complexity—0
Sample-Efficient Learning of Mixtures—0
Sample-efficient proper PAC learning with approximate differential privacy—0
Sample-Optimal PAC Learning of Halfspaces with Malicious Noise—0
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