1 citations · 1 across the 2 of their papers we have counts for
3 papers
stat.ML2022★ 1 cited
Universally Consistent Online Learning with Arbitrarily Dependent Responses
Steve Hanneke
This work provides an online learning rule that is universally consistent under processes on (X,Y) pairs, under conditions only on the X process. As a special case, the conditions…
cs.LG2021
Open Problem: Is There an Online Learning Algorithm That Learns Whenever Online Learning Is Possible?
Steve Hanneke
This open problem asks whether there exists an online learning algorithm for binary classification that guarantees, for all target concepts, to make a sublinear number of mistakes,…
cs.LG2018
Actively Avoiding Nonsense in Generative Models
Steve Hanneke, Adam Kalai, Gautam Kamath +1
A generative model may generate utter nonsense when it is fit to maximize the likelihood of observed data. This happens due to "model error," i.e., when the true data generating di…