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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
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
Screw Geometry Meets Bandits: Incremental Acquisition of Demonstrations to Generate Manipulation Plans—0
Semi-verified PAC Learning from the Crowd—0
Sequential Mode Estimation with Oracle Queries—0
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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