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PAC learning

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

Papers

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Showing 126–150 of 289 papers

TitleStatusHype
Superpolynomial Lower Bounds for Decision Tree Learning and Testing—0
Unified Algorithms for RL with Decision-Estimation Coefficients: PAC, Reward-Free, Preference-Based Learning, and Beyond—0
Analyzing Robustness of Angluin's L* Algorithm in Presence of Noise—0
On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Data—0
Fine-Grained Distribution-Dependent Learning Curves—0
Cryptographic Hardness of Learning Halfspaces with Massart Noise—0
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
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
On Learning and Enforcing Latent Assessment Models using Binary Feedback from Human Auditors Regarding Black-Box Classifiers—0
A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability—0
Monotone Learning—0
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks—0
Planted Dense Subgraphs in Dense Random Graphs Can Be Recovered using Graph-based Machine LearningCode0
Efficiently Learning One Hidden Layer ReLU Networks From Queries—0
Exponential Separation between Two Learning Models and Adversarial Robustness—0
On computable learning of continuous features—0
PAC-Learning Uniform Ergodic Communicative Networks—0
Realizable Learning is All You Need—0
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