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PAC learning

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

Papers

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Showing 1–25 of 289 papers

TitleStatusHype
VICE: Variational Interpretable Concept EmbeddingsCode1
Prospective Learning: Learning for a Dynamic FutureCode1
Lean Formalization of Generalization Error Bound by Rademacher ComplexityCode1
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean EstimationCode0
SAT-Based PAC Learning of Description Logic ConceptsCode0
Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic ModelsCode0
Planted Dense Subgraphs in Dense Random Graphs Can Be Recovered using Graph-based Machine LearningCode0
Introduction to Machine Learning: Class Notes 67577Code0
SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term MemoryCode0
Towards a theory of model distillationCode0
Optimistic Rates for Learning from Label ProportionsCode0
Regression EquilibriumCode0
Quantum Boosting using Domain-Partitioning HypothesesCode0
Agnostic Learning of a Single Neuron with Gradient Descent—0
Agnostic Learning by Refuting—0
Active-learning-based non-intrusive Model Order Reduction—0
Adversarial Robustness: What fools you makes you stronger—0
Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds—0
A Computational Separation between Private Learning and Online Learning—0
A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability—0
A Linear Theory of Multi-Winner Voting—0
Algorithms and SQ Lower Bounds for Robustly Learning Real-valued Multi-index Models—0
An Active Learning Framework for Constructing High-fidelity Mobility Maps—0
Analyzing Robustness of Angluin's L* Algorithm in Presence of Noise—0
Adversarially Robust Learning with Tolerance—0
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