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PAC learning

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

Papers

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Showing 101–150 of 289 papers

TitleStatusHype
Hardness of Online Sleeping Combinatorial Optimization Problems—0
High-arity PAC learning via exchangeability—0
How to Use Heuristics for Differential Privacy—0
Identifying Information from Observations with Uncertainty and Novelty—0
Implicit High-Order Moment Tensor Estimation and Learning Latent Variable Models—0
Improved Algorithms for Collaborative PAC Learning—0
Incentive-aware PAC learning—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
Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise—0
Information-theoretic generalization bounds for learning from quantum data—0
Is Efficient PAC Learning Possible with an Oracle That Responds 'Yes' or 'No'?—0
Is Out-of-Distribution Detection Learnable?—0
Is Transductive Learning Equivalent to PAC Learning?—0
Learnability can be undecidable—0
Transductive Learning Is Compact—0
Learnability with PAC Semantics for Multi-agent Beliefs—0
Learnable: Theory vs Applications—0
Learning and Certification under Instance-targeted Poisoning—0
Learning, compression, and leakage: Minimising classification error via meta-universal compression principles—0
Learning Concepts Definable in First-Order Logic with Counting—0
Learning DNF Expressions from Fourier Spectrum—0
Learning from Mixtures of Private and Public Populations—0
Learning Geometric Concepts with Nasty Noise—0
Efficient PAC Learning from the Crowd with Pairwise Comparisons—0
Learning Halfspaces with the Zero-One Loss: Time-Accuracy Tradeoffs—0
Learning Halfspaces with Tsybakov Noise—0
Learning Linear Attention in Polynomial Time—0
Learning multivariate Gaussians with imperfect advice—0
Learning Neural Networks with Two Nonlinear Layers in Polynomial Time—0
Learning Noisy Halfspaces with a Margin: Massart is No Harder than Random—0
Learning pseudo-Boolean k-DNF and Submodular Functions—0
Learning Query Inseparable ELH Ontologies—0
Learning the hypotheses space from data through a U-curve algorithm—0
Learning the Hypotheses Space from data: Learning Space and U-curve Property—0
Learning Time Dependent Choice—0
Is Nash Equilibrium Approximator Learnable?—0
Learning under p-Tampering Attacks—0
Learning versus Refutation in Noninteractive Local Differential Privacy—0
Locally Private Learning without Interaction Requires Separation—0
Lifting uniform learners via distributional decomposition—0
List Learning with Attribute Noise—0
List Sample Compression and Uniform Convergence—0
From learnable objects to learnable random objects—0
Lower Bounds for Adversarially Robust PAC Learning—0
Low-Rank MDPs with Continuous Action Spaces—0
Majority-of-Three: The Simplest Optimal Learner?—0
Markov Decision Processes with Continuous Side Information—0
Measurability in the Fundamental Theorem of Statistical Learning—0
Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability—0
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