Uploaded March 2026 | Updated September 2026, 2 weeks ago
Samory Kpotufe (Columbia University)
https://simons.berkeley.edu/talks/samory-kpotufe-columbia-university-2026-02-26
Learning from Heterogeneous Sources
Multitask Learning refers to the problem of aggregating many datasets from separate source distributions to improve performance on a target prediction task. Our aim is to understand (1) sufficient and necessary conditions for speedup in convergence rates over vanilla prediction with just the target data, (2) how such speedup depends on the number of datasets and samples per dataset, and (3) whether such speedup is achievable adaptively, i.e., by procedures with no prior distributional information.
The picture turns out to be mixed as the problem displays sharp gaps between oracle rates and adaptive rates, i.e., there exist situations where no procedure can do better than using the target data alone even though a large subset of datasets are informative about the target task. On the other hand, a bit of information on the relation between sources and target distributions can allow for near optimal adaptive rates. These results lead to many interesting new questions which I'll attempt to properly convey.
The talk is based on various works with collaborators such as S. Hanneke, A. Gretton, M. Z. Li, D. Meunier.
Samory Kpotufe (Columbia University)
https://simons.berkeley.edu/talks/samory-kpotufe-columbia-university-2026-02-26
Learning from Heterogeneous Sources
Multitask Learning refers to the problem of aggregating many datasets from separate source distributions to improve performance on a target prediction task. Our aim is to understand (1) sufficient and necessary conditions for speedup in convergence rates over vanilla prediction with just the target data, (2) how such speedup depends on the number of datasets and samples per dataset, and (3) whether such speedup is achievable adaptively, i.e., by procedures with no prior distributional information.
The picture turns out to be mixed as the problem displays sharp gaps between oracle rates and adaptive rates, i.e., there exist situations where no procedure can do better than using the target data alone even though a large subset of datasets are informative about the target task. On the other hand, a bit of information on the relation between sources and target distributions can allow for near optimal adaptive rates. These results lead to many interesting new questions which I'll attempt to properly convey.
The talk is based on various works with collaborators such as S. Hanneke, A. Gretton, M. Z. Li, D. Meunier.
