SOTAVerified

PAC learning

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–10 of 289 papers

TitleStatusHype
Computational-Statistical Tradeoffs from NP-hardness—0
A Distributional-Lifting Theorem for PAC Learning—0
Conservative classifiers do consistently well with improving agents: characterizing statistical and online learning—0
Attribute-Efficient PAC Learning of Sparse Halfspaces with Constant Malicious Noise Rate—0
Algorithms and SQ Lower Bounds for Robustly Learning Real-valued Multi-index Models—0
A packing lemma for VCN_k-dimension and learning high-dimensional data—0
From learnable objects to learnable random objects—0
Lean Formalization of Generalization Error Bound by Rademacher ComplexityCode1
Numerical and statistical analysis of NeuralODE with Runge-Kutta time integration—0
Towards Understanding Multi-Round Large Language Model Reasoning: Approximability, Learnability and Generalizability—0
Show:102550
← PrevPage 1 of 29Next →

No leaderboard results yet.