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PAC learning

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

Papers

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Showing 151–160 of 289 papers

TitleStatusHype
Quantum Boosting using Domain-Partitioning HypothesesCode0
Active Learning for Contextual Search with Binary Feedbacks—0
Towards a theory of out-of-distribution learning—0
Learning the hypotheses space from data through a U-curve algorithm—0
Learning General Halfspaces with General Massart Noise under the Gaussian Distribution—0
Is Nash Equilibrium Approximator Learnable?—0
Statistically Near-Optimal Hypothesis Selection—0
On the Power of Differentiable Learning versus PAC and SQ Learning—0
Multiclass versus Binary Differentially Private PAC Learning—0
A Theory of PAC Learnability of Partial Concept Classes—0
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