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PAC learning

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

Papers

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Showing 261–270 of 289 papers

TitleStatusHype
Learning General Halfspaces with General Massart Noise under the Gaussian Distribution—0
Tight Bounds for Collaborative PAC Learning via Multiplicative Weights—0
Tight Bounds on Low-degree Spectral Concentration of Submodular and XOS functions—0
Tight Lower Bounds for Locally Differentially Private Selection—0
Towards a combinatorial characterization of bounded memory learning—0
Towards a Combinatorial Characterization of Bounded-Memory Learning—0
Towards a theory of out-of-distribution learning—0
Towards Efficient Contrastive PAC Learning—0
Towards Understanding Multi-Round Large Language Model Reasoning: Approximability, Learnability and Generalizability—0
Tree Learning: Optimal Algorithms and Sample Complexity—0
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