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PAC learning

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

Papers

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Showing 201–250 of 289 papers

TitleStatusHype
Agnostic Learning of a Single Neuron with Gradient Descent—0
Proper Learning, Helly Number, and an Optimal SVM Bound—0
On the Complexity of Learning from Label Proportions—0
Closure Properties for Private Classification and Online Prediction—0
An Active Learning Framework for Constructing High-fidelity Mobility Maps—0
Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds—0
Decidability of Sample Complexity of PAC Learning in finite setting—0
On the Sample Complexity of Adversarial Multi-Source PAC Learning—0
Quantum statistical query learning—0
Best-item Learning in Random Utility Models with Subset Choices—0
Towards a combinatorial characterization of bounded memory learning—0
On Learnability with Computable Learners—0
Learning the Hypotheses Space from data: Learning Space and U-curve Property—0
On the Sample Complexity of Learning Sum-Product Networks—0
PAC learning with stable and private predictions—0
Sequential Mode Estimation with Oracle Queries—0
Learning Query Inseparable ELH Ontologies—0
On Generalization Bounds of a Family of Recurrent Neural Networks—0
Learning Concepts Definable in First-Order Logic with Counting—0
The Power of Comparisons for Actively Learning Linear Classifiers—0
Distribution-Independent PAC Learning of Halfspaces with Massart Noise—0
Query-driven PAC-Learning for Reasoning—0
Lower Bounds for Adversarially Robust PAC Learning—0
Private Hypothesis Selection—0
Regression EquilibriumCode0
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
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