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PAC learning

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

Papers

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Showing 241–250 of 289 papers

TitleStatusHype
Simple and Fast Algorithms for Interactive Machine Learning with Random Counter-examples—0
Simplifying Adversarially Robust PAC Learning with Tolerance—0
Simultaneous Private Learning of Multiple Concepts—0
Small Covers for Near-Zero Sets of Polynomials and Learning Latent Variable Models—0
SQ Lower Bounds for Learning Single Neurons with Massart Noise—0
Stability is Stable: Connections between Replicability, Privacy, and Adaptive Generalization—0
Statistically Near-Optimal Hypothesis Selection—0
Strategic Classification With Externalities—0
Superconstant Inapproximability of Decision Tree Learning—0
Super Non-singular Decompositions of Polynomials and their Application to Robustly Learning Low-degree PTFs—0
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