SOTAVerified

PAC learning

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 276–289 of 289 papers

TitleStatusHype
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
Quantum Boosting using Domain-Partitioning HypothesesCode0
Regression EquilibriumCode0
Introduction to Machine Learning: Class Notes 67577Code0
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean EstimationCode0
Towards a theory of model distillationCode0
SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term MemoryCode0
Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic ModelsCode0
Optimistic Rates for Learning from Label ProportionsCode0
SAT-Based PAC Learning of Description Logic ConceptsCode0
Show:102550
← PrevPage 12 of 12Next →

No leaderboard results yet.