3 citations · 3 across the 1 of their papers we have counts for
2 papers
stat.ML2017
Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes
Steve Hanneke
This work initiates a general study of learning and generalization without the i.i.d. assumption, starting from first principles. While the traditional approach to statistical lear…
cs.LG2015★ 3 cited
Refined Error Bounds for Several Learning Algorithms
Steve Hanneke
This article studies the achievable guarantees on the error rates of certain learning algorithms, with particular focus on refining logarithmic factors. Many of the results are bas…