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PAC learning

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

Papers

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Showing 1–10 of 289 papers

Title	Date	Tasks	Status	Hype	Score
Prospective Learning: Learning for a Dynamic Future	Oct 31, 2024	PAC learning	CodeCode Available	1	5
Lean Formalization of Generalization Error Bound by Rademacher Complexity	Mar 25, 2025	LEMMAPAC learning	CodeCode Available	1	5
VICE: Variational Interpretable Concept Embeddings	May 2, 2022	Experimental DesignObject	CodeCode Available	1	5
SAT-Based PAC Learning of Description Logic Concepts	May 15, 2023	PAC learning	CodeCode Available	0	5
Quantum Boosting using Domain-Partitioning Hypotheses	Oct 25, 2021	BenchmarkingEnsemble Learning	CodeCode Available	0	5
Regression Equilibrium	May 4, 2019	PAC learningPrediction	CodeCode Available	0	5
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation	Nov 1, 2022	Computational EfficiencyPAC learning	CodeCode Available	0	5
Introduction to Machine Learning: Class Notes 67577	Apr 23, 2009	BIG-bench Machine LearningClustering	CodeCode Available	0	5
Planted Dense Subgraphs in Dense Random Graphs Can Be Recovered using Graph-based Machine Learning	Jan 5, 2022	Graph Neural NetworkPAC learning	CodeCode Available	0	5
Optimistic Rates for Learning from Label Proportions	Jun 1, 2024	PAC learningWeakly-supervised Learning	CodeCode Available	0	5

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