An Optimal Agnostic PAC Algorithm
The paper claims an agnostic PAC learner that matches known lower bounds up to universal constants.
For hypothesis classes with finite VC dimension, the authors give a risk guarantee depending on the optimal class risk, dimension, confidence, and sample size. The bound holds with probability at least \(1-\delta\) from an i.i.d. sample. They say this settles the sample complexity of agnostic PAC learning at each fixed \(L^*\), up to constants. ArXiv · AI/CL/LG's note
For hypothesis classes with finite VC dimension, the authors give a risk guarantee depending on the optimal class risk, dimension, confidence, and sample size. The bound holds with probability at least \(1-\delta\) from an i.i.d. sample. They say this settles the sample complexity of agnostic PAC learning at each fixed \(L^*\), up to constants. ArXiv · AI/CL/LG's note
score 6