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PAC learning

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

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Showing 76–100 of 289 papers

TitleStatusHype
Efficient PAC Learning of Halfspaces with Constant Malicious Noise Rate—0
Efficient Statistics With Unknown Truncation, Polynomial Time Algorithms, Beyond Gaussians—0
Ehrenfeucht-Haussler Rank and Chain of Thought—0
Enhancing PAC Learning of Half spaces Through Robust Optimization Techniques—0
Ensuring superior learning outcomes and data security for authorized learner—0
Error Exponent in Agnostic PAC Learning—0
Exponential Separation between Two Learning Models and Adversarial Robustness—0
Fairness-Aware PAC Learning from Corrupted Data—0
Fast Collaborative Filtering from Implicit Feedback with Provable Guarantees—0
Fast decision tree learning solves hard coding-theoretic problems—0
Faster PAC Learning and Smaller Coresets via Smoothed Analysis—0
Fast Hyperparameter Tuning using Bayesian Optimization with Directional Derivatives—0
Fast Rates for Bandit PAC Multiclass Classification—0
Probably Approximately Correct Federated Learning—0
Fill In The Gaps: Model Calibration and Generalization with Synthetic Data—0
Find a witness or shatter: the landscape of computable PAC learning—0
Fine-Grained Distribution-Dependent Learning Curves—0
Forster Decomposition and Learning Halfspaces with Noise—0
From Local Pseudorandom Generators to Hardness of Learning—0
From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model—0
Generalization Bounds for Data-Driven Numerical Linear Algebra—0
Hardness of Learning Boolean Functions from Label Proportions—0
Near-Optimal Statistical Query Hardness of Learning Halfspaces with Massart Noise—0
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks—0
Distribution Learning Meets Graph Structure Sampling—0
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