3 papers
cs.LG2026
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…
cs.LG2026
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…
stat.ML2025
Adaptive Sample Aggregation In Transfer Learning
Steve Hanneke, Samory Kpotufe
Transfer Learning aims to optimally aggregate samples from a target distribution, with related samples from a so-called source distribution to improve target risk. Multiple procedu…