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PAC learning

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

Papers

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Showing 51–100 of 289 papers

TitleStatusHype
Revisiting Agnostic PAC Learning—0
Ramsey Theorems for Trees and a General 'Private Learning Implies Online Learning' Theorem—0
Superconstant Inapproximability of Decision Tree Learning—0
Distribution Learnability and Robustness—0
Credit Attribution and Stable Compression—0
Fast Rates for Bandit PAC Multiclass Classification—0
Is Efficient PAC Learning Possible with an Oracle That Responds 'Yes' or 'No'?—0
On the Computability of Robust PAC Learning—0
Optimistic Rates for Learning from Label ProportionsCode0
Weak Robust Compatibility Between Learning Algorithms and Counterfactual Explanation Generation Algorithms—0
On the Computational Landscape of Replicable Learning—0
Distribution Learning Meets Graph Structure Sampling—0
Efficient PAC Learnability of Dynamical Systems Over Multilayer Networks—0
Is Transductive Learning Equivalent to PAC Learning?—0
Error Exponent in Agnostic PAC Learning—0
On the Power of Interactive Proofs for Learning—0
On the Learnability of Out-of-distribution Detection—0
Super Non-singular Decompositions of Polynomials and their Application to Robustly Learning Low-degree PTFs—0
Hardness of Learning Boolean Functions from Label Proportions—0
List Sample Compression and Uniform Convergence—0
Towards a theory of model distillationCode0
Majority-of-Three: The Simplest Optimal Learner?—0
Proper vs Improper Quantum PAC learning—0
High-arity PAC learning via exchangeability—0
Private PAC Learning May be Harder than Online Learning—0
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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