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PAC learning

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

Papers

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Showing 176–200 of 289 papers

TitleStatusHype
On the Hardness of PAC-learning Stabilizer States with Noise—0
On the Learnability of Out-of-distribution Detection—0
On the Power of Differentiable Learning versus PAC and SQ Learning—0
On the Power of Interactive Proofs for Learning—0
On the Power of Learning from k-Wise Queries—0
On the Role of Entanglement and Statistics in Learning—0
On the Role of Noise in the Sample Complexity of Learning Recurrent Neural Networks: Exponential Gaps for Long Sequences—0
On the Sample Complexity of Adversarial Multi-Source PAC Learning—0
On the Sample Complexity of Learning Sum-Product Networks—0
Sample Efficient Algorithms for Learning Quantum Channels in PAC Model and the Approximate State Discrimination Problem—0
Optimal Bounds on Approximation of Submodular and XOS Functions by Juntas—0
Optimal Learners for Realizable Regression: PAC Learning and Online Learning—0
Optimal lower bounds for Quantum Learning via Information Theory—0
Order-Revealing Encryption and the Hardness of Private Learning—0
Overview of AdaBoost : Reconciling its views to better understand its dynamics—0
PAC Generalization via Invariant Representations—0
PAC learning and stabilizing Hedonic Games: towards a unifying approach—0
PAC Learning-Based Verification and Model Synthesis—0
PAC-Learning for Strategic Classification—0
PAC-learning gains of Turing machines over circuits and neural networks—0
PAC Learning Guarantees Under Covariate Shift—0
PAC-learning in the presence of adversaries—0
PAC-learning in the presence of evasion adversaries—0
PAC Learning is just Bipartite Matching (Sort of)—0
PAC-learning is Undecidable—0
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