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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
Adversarial Laws of Large Numbers and Optimal Regret in Online Classification—0
From Local Pseudorandom Generators to Hardness of Learning—0
The Price is (Probably) Right: Learning Market Equilibria from Samples—0
Communication-Aware Collaborative Learning—0
Near-Optimal Statistical Query Hardness of Learning Halfspaces with Massart Noise—0
Small Covers for Near-Zero Sets of Polynomials and Learning Latent Variable Models—0
Sample-efficient proper PAC learning with approximate differential privacy—0
VC Dimension and Distribution-Free Sample-Based Testing—0
PAC-Learning for Strategic Classification—0
Towards a Combinatorial Characterization of Bounded-Memory Learning—0
Efficient PAC Learning from the Crowd with Pairwise Comparisons—0
Reducing Adversarially Robust Learning to Non-Robust PAC Learning—0
Learning, compression, and leakage: Minimising classification error via meta-universal compression principles—0
SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term MemoryCode0
A Polynomial Time Algorithm for Learning Halfspaces with Tsybakov Noise—0
Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic ModelsCode0
Learning from Mixtures of Private and Public Populations—0
A Computational Separation between Private Learning and Online Learning—0
Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks—0
An Optimal Elimination Algorithm for Learning a Best Arm—0
Learning Halfspaces with Tsybakov Noise—0
List Learning with Attribute Noise—0
Faster PAC Learning and Smaller Coresets via Smoothed Analysis—0
Probably Approximately Correct Constrained Learning—0
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