SOTAVerified

PAC learning

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 271–280 of 289 papers

TitleStatusHype
The sample complexity of multi-distribution learning—0
The VC-Dimension of Similarity Hypotheses Spaces—0
The working principles of model-based GAs fall within the PAC framework: A mathematical theory of problem decomposition—0
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
Planted Dense Subgraphs in Dense Random Graphs Can Be Recovered using Graph-based Machine LearningCode0
Show:102550
← PrevPage 28 of 29Next →

No leaderboard results yet.