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PAC learning

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

Papers

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Showing 201–225 of 289 papers

TitleStatusHype
PAC Learning Linear Thresholds from Label Proportions—0
PAC-Learning Uniform Ergodic Communicative Networks—0
PAC Learning, VC Dimension, and the Arithmetic Hierarchy—0
PAC Learning with Improvements—0
PAC learning with nasty noise—0
PAC learning with stable and private predictions—0
PAC Verification of Statistical Algorithms—0
Policy Synthesis and Reinforcement Learning for Discounted LTL—0
Predicting with Distributions—0
Predictive PAC Learning and Process Decompositions—0
Privacy-preserving Prediction—0
Private Hypothesis Selection—0
Private learning implies quantum stability—0
Private PAC learning implies finite Littlestone dimension—0
Private PAC Learning May be Harder than Online Learning—0
On Proper Learnability between Average- and Worst-case Robustness—0
Probably Approximately Correct Constrained Learning—0
Probably approximately correct high-dimensional causal effect estimation given a valid adjustment set—0
Probably Approximately Precision and Recall Learning—0
Proper Learning, Helly Number, and an Optimal SVM Bound—0
Proper vs Improper Quantum PAC learning—0
Provable learning of quantum states with graphical models—0
Quantum hardness of learning shallow classical circuits—0
Quantum statistical query learning—0
Query-driven PAC-Learning for Reasoning—0
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