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PAC learning

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

Papers

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Showing 251–289 of 289 papers

TitleStatusHype
Collaborative PAC Learning—0
Markov Decision Processes with Continuous Side Information—0
A learning problem that is independent of the set theory ZFC axioms—0
Learning under p-Tampering Attacks—0
An Approach to One-Bit Compressed Sensing Based on Probably Approximately Correct Learning Theory—0
Learning Neural Networks with Two Nonlinear Layers in Polynomial Time—0
Agnostic Learning by Refuting—0
Learning Geometric Concepts with Nasty Noise—0
Sample-Efficient Learning of Mixtures—0
On Fundamental Limits of Robust Learning—0
Efficient PAC Learning from the Crowd—0
On the Power of Learning from k-Wise Queries—0
Multi-step learning and underlying structure in statistical models—0
Predicting with Distributions—0
Simultaneous Private Learning of Multiple Concepts—0
PAC Learning-Based Verification and Model Synthesis—0
Fast Collaborative Filtering from Implicit Feedback with Provable Guarantees—0
Hardness of Online Sleeping Combinatorial Optimization Problems—0
The Optimal Sample Complexity of PAC Learning—0
Order-Revealing Encryption and the Hardness of Private Learning—0
Differentially Private Release and Learning of Threshold Functions—0
Tight Bounds on Low-degree Spectral Concentration of Submodular and XOS functions—0
The VC-Dimension of Similarity Hypotheses Spaces—0
PAC Learning, VC Dimension, and the Arithmetic Hierarchy—0
Online Learning of k-CNF Boolean Functions—0
Sample Complexity Bounds on Differentially Private Learning via Communication Complexity—0
Distribution-Independent Reliable Learning—0
Characterizing the Sample Complexity of Private Learners—0
More data speeds up training time in learning halfspaces over sparse vectors—0
Predictive PAC Learning and Process Decompositions—0
Optimal Bounds on Approximation of Submodular and XOS Functions by Juntas—0
Representation, Approximation and Learning of Submodular Functions Using Low-rank Decision Trees—0
Learning Halfspaces with the Zero-One Loss: Time-Accuracy Tradeoffs—0
Learning pseudo-Boolean k-DNF and Submodular Functions—0
Learning DNF Expressions from Fourier Spectrum—0
A Unified Framework for Approximating and Clustering Data—0
A Complete Characterization of Statistical Query Learning with Applications to Evolvability—0
Introduction to Machine Learning: Class Notes 67577Code0
PAC learning with nasty noise—0
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