SOTAVerified

PAC learning

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 131–140 of 289 papers

TitleStatusHype
Generalization Bounds for Data-Driven Numerical Linear Algebra—0
PAC Generalization via Invariant Representations—0
Bézier Flow: a Surface-wise Gradient Descent Method for Multi-objective Optimization—0
Sample Complexity Bounds for Robustly Learning Decision Lists against Evasion Attacks—0
VICE: Variational Interpretable Concept EmbeddingsCode1
Clifford Circuits can be Properly PAC Learned if and only if RP=NP—0
Active-learning-based non-intrusive Model Order Reduction—0
Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability—0
A Characterization of Multiclass Learnability—0
Adversarially Robust Learning with Tolerance—0
Show:102550
← PrevPage 14 of 29Next →

No leaderboard results yet.