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PAC learning

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

Papers

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Showing 101–125 of 289 papers

TitleStatusHype
On the Role of Noise in the Sample Complexity of Learning Recurrent Neural Networks: Exponential Gaps for Long Sequences—0
Policy Synthesis and Reinforcement Learning for Discounted LTL—0
SAT-Based PAC Learning of Description Logic ConceptsCode0
A Parameterized Theory of PAC Learning—0
Probably Approximately Correct Federated Learning—0
Online Learning and Disambiguations of Partial Concept Classes—0
Lifting uniform learners via distributional decomposition—0
Stability is Stable: Connections between Replicability, Privacy, and Adaptive Generalization—0
Agnostic PAC Learning of k-juntas Using L2-Polynomial Regression—0
On the complexity of PAC learning in Hilbert spaces—0
Do PAC-Learners Learn the Marginal Distribution?—0
Tree Learning: Optimal Algorithms and Sample Complexity—0
Find a witness or shatter: the landscape of computable PAC learning—0
PAC learning and stabilizing Hedonic Games: towards a unifying approach—0
Optimal lower bounds for Quantum Learning via Information Theory—0
A Strongly Polynomial Algorithm for Approximate Forster Transforms and its Application to Halfspace Learning—0
Bagging is an Optimal PAC Learner—0
PAC Verification of Statistical Algorithms—0
Comparative Learning: A Sample Complexity Theory for Two Hypothesis Classes—0
On Proper Learnability between Average- and Worst-case Robustness—0
A Characterization of List Learnability—0
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean EstimationCode0
Is Out-of-Distribution Detection Learnable?—0
Learning versus Refutation in Noninteractive Local Differential Privacy—0
SQ Lower Bounds for Learning Single Neurons with Massart Noise—0
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