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PAC learning

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

Papers

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Showing 76–100 of 289 papers

TitleStatusHype
Collaborative Learning with Different Labeling Functions—0
Transductive Learning Is Compact—0
The sample complexity of multi-distribution learning—0
-fractional Core Stability in Hedonic Games—0
Information-theoretic generalization bounds for learning from quantum data—0
Low-Rank MDPs with Continuous Action Spaces—0
A PAC Learning Algorithm for LTL and Omega-regular Objectives in MDPs—0
PAC Learning Linear Thresholds from Label Proportions—0
Overview of AdaBoost : Reconciling its views to better understand its dynamics—0
Distributional PAC-Learning from Nisan's Natural Proofs—0
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds—0
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds—0
User-Level Differential Privacy With Few Examples Per User—0
Provable learning of quantum states with graphical models—0
Computing the Vapnik Chervonenkis Dimension for Non-Discrete Settings—0
Efficiently Learning One-Hidden-Layer ReLU Networks via Schur Polynomials—0
The Sample Complexity of Multi-Distribution Learning for VC Classes—0
Optimal Learners for Realizable Regression: PAC Learning and Online Learning—0
Multiclass Boosting: Simple and Intuitive Weak Learning Criteria—0
Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise—0
Learnability with PAC Semantics for Multi-agent Beliefs—0
On the Role of Entanglement and Statistics in Learning—0
Agnostic Multi-Group Active Learning—0
Attribute-Efficient PAC Learning of Low-Degree Polynomial Threshold Functions with Nasty Noise—0
On the Role of Noise in the Sample Complexity of Learning Recurrent Neural Networks: Exponential Gaps for Long Sequences—0
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