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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
Computational-Statistical Tradeoffs from NP-hardness—0
Computing the Vapnik Chervonenkis Dimension for Non-Discrete Settings—0
Conservative classifiers do consistently well with improving agents: characterizing statistical and online learning—0
Contrastive Learning with Nasty Noise—0
Credit Attribution and Stable Compression—0
Crowdsourced PAC Learning under Classification Noise—0
Cryptographic Hardness of Learning Halfspaces with Massart Noise—0
Data-Driven Neural Certificate Synthesis—0
Decidability of Sample Complexity of PAC Learning in finite setting—0
Derandomizing Multi-Distribution Learning—0
Differentially Private Learning of Geometric Concepts—0
Differentially Private Release and Learning of Threshold Functions—0
Attribute-Efficient PAC Learning of Sparse Halfspaces with Constant Malicious Noise Rate—0
Attribute-Efficient PAC Learning of Low-Degree Polynomial Threshold Functions with Nasty Noise—0
Agnostic Smoothed Online Learning—0
A Distributional-Lifting Theorem for PAC Learning—0
Effective Littlestone Dimension—0
Do PAC-Learners Learn the Marginal Distribution?—0
A Theory of PAC Learnability of Partial Concept Classes—0
Distribution-Specific Agnostic Conditional Classification With Halfspaces—0
Efficiently Learning One Hidden Layer ReLU Networks From Queries—0
Efficiently Learning One-Hidden-Layer ReLU Networks via Schur Polynomials—0
Efficient Optimal PAC Learning—0
Efficient PAC Learnability of Dynamical Systems Over Multilayer Networks—0
Efficient PAC Learning from the Crowd—0
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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