SOTAVerified

PAC learning

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 226–250 of 289 papers

TitleStatusHype
Quantum hardness of learning shallow classical circuits—0
From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model—0
Differentially Private Learning of Geometric Concepts—0
Crowdsourced PAC Learning under Classification Noise—0
Fast Hyperparameter Tuning using Bayesian Optimization with Directional Derivatives—0
Can SGD Learn Recurrent Neural Networks with Provable Generalization?—0
Learnability can be undecidable—0
PAC Learning Guarantees Under Covariate Shift—0
PAC-learning in the presence of adversaries—0
How to Use Heuristics for Differential Privacy—0
Sample Efficient Algorithms for Learning Quantum Channels in PAC Model and the Approximate State Discrimination Problem—0
Simple and Fast Algorithms for Interactive Machine Learning with Random Counter-examples—0
Locally Private Learning without Interaction Requires Separation—0
Learning Time Dependent Choice—0
Wasserstein Soft Label Propagation on Hypergraphs: Algorithm and Generalization Error Bounds—0
PAC-learning is Undecidable—0
Learnable: Theory vs Applications—0
AI Reasoning Systems: PAC and Applied Methods—0
PAC-learning in the presence of evasion adversaries—0
Private PAC learning implies finite Littlestone dimension—0
Tight Bounds for Collaborative PAC Learning via Multiplicative Weights—0
Improved Algorithms for Collaborative PAC Learning—0
Privacy-preserving Prediction—0
Tight Lower Bounds for Locally Differentially Private Selection—0
Multi-label Learning for Large Text Corpora using Latent Variable Model with Provable Gurantees—0
Show:102550
← PrevPage 10 of 12Next →

No leaderboard results yet.