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PAC learning

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

Papers

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Showing 171–180 of 289 papers

TitleStatusHype
Private learning implies quantum stability—0
Sample-Optimal PAC Learning of Halfspaces with Malicious Noise—0
On Agnostic PAC Learning using L_2-polynomial Regression and Fourier-based Algorithms—0
Fairness-Aware PAC Learning from Corrupted Data—0
Adversarial Robustness: What fools you makes you stronger—0
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
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