16 citations · 21 across the 6 of their papers we have counts for
6 papers
Universal Multiclass Transductive Online Learning
Steve Hanneke, Hongao Wang
We consider the problem of universal transductive online classification with a possibly unbounded label space. This setting considers online learning, with the sequence of instance…
When More Data Doesn't Help: Limits of Adaptation in Multitask Learning
Steve Hanneke, Mingyue Xu
Multitask learning and related frameworks have achieved tremendous success in modern applications. In multitask learning problem, we are given a set of heterogeneous datasets colle…
Help Me Help You: The Aggregate Value of Source and Target Data in Transfer Learning
Steve Hanneke, Samory Kpotufe
Transfer learning, in regression or classification, concerns problems where one aims to use imperfect data from a source distribution P to improve prediction w.r.t. a target distri…
Limits of Model Selection under Transfer Learning
Steve Hanneke, Samory Kpotufe, Yasaman Mahdaviyeh
Theoretical studies on transfer learning or domain adaptation have so far focused on situations with a known hypothesis class or model; however in practice, some amount of model se…
A No-Free-Lunch Theorem for MultiTask Learning
Steve Hanneke, Samory Kpotufe
Multitask learning and related areas such as multi-source domain adaptation address modern settings where datasets from related distributions are to be combined towar…
On the Value of Target Data in Transfer Learning
Steve Hanneke, Samory Kpotufe
We aim to understand the value of additional labeled or unlabeled target data in transfer learning, for any given amount of source data; this is motivated by practical questions ar…