---
res:
  bibo_abstract:
  - "We present new fast-rate PAC-Bayesian generalization bounds for multi-task and\r\nmeta-learning
    in the unbalanced setting, i.e. when the tasks have training sets of\r\ndifferent
    sizes, as is typically the case in real-world scenarios. Previously, only\r\nstandard-rate
    bounds were known for this situation, while fast-rate bounds were\r\nlimited to
    the setting where all training sets are of equal size. Our new bounds\r\nare numerically
    computable as well as interpretable, and we demonstrate their\r\nflexibility in
    handling a number of cases where they give stronger guarantees\r\nthan previous
    bounds. Besides the bounds themselves, we also make conceptual\r\ncontributions:
    we demonstrate that the unbalanced multi-task setting has different\r\nstatistical
    properties than the balanced situation, specifically that proofs from\r\nthe balanced
    situation do not carry over to the unbalanced setting. Additionally,\r\nwe shed
    light on the fact that the unbalanced situation allows two meaningful\r\ndefinitions
    of multi-task risk, depending on whether all tasks should be considered\r\nequally
    important or if sample-rich tasks should receive more weight than samplepoor ones.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Hossein
      foaf_name: Zakerinia, Hossein
      foaf_surname: Zakerinia
      foaf_workInfoHomepage: http://www.librecat.org/personId=653bd8b6-f394-11eb-9cf6-c0bbf6cd78d4
    orcid: 0009-0007-3977-6462
  - foaf_Person:
      foaf_givenName: Christoph
      foaf_name: Lampert, Christoph
      foaf_surname: Lampert
      foaf_workInfoHomepage: http://www.librecat.org/personId=40C20FD2-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0001-8622-7887
  bibo_doi: 10.52202/085713-0278
  bibo_volume: 38
  dct_date: 2025^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/1049-5258
  - http://id.crossref.org/issn/9798331338275
  dct_language: eng
  dct_publisher: Neural Information Processing Systems Foundation@
  dct_title: Fast rate bounds for multi-task and meta-learning with different sample
    sizes@
...