2 papers
cs.LG2021
A Computability Perspective on (Verified) Machine Learning
Tonicha Crook, Jay Morgan, Arno Pauly +1
There is a strong consensus that combining the versatility of machine learning with the assurances given by formal verification is highly desirable. It is much less clear what veri…
cs.LG2018
Cost-Aware Learning for Improved Identifiability with Multiple Experiments
Longyun Guo, Jean Honorio, John Morgan
We analyze the sample complexity of learning from multiple experiments where the experimenter has a total budget for obtaining samples. In this problem, the learner should choose a…