---
_id: '68'
abstract:
- lang: eng
  text: The most common assumption made in statistical learning theory is the assumption
    of the independent and identically distributed (i.i.d.) data. While being very
    convenient mathematically, it is often very clearly violated in practice. This
    disparity between the machine learning theory and applications underlies a growing
    demand in the development of algorithms that learn from dependent data and theory
    that can provide generalization guarantees similar to the independent situations.
    This thesis is dedicated to two variants of dependencies that can arise in practice.
    One is a dependence on the level of samples in a single learning task. Another
    dependency type arises in the multi-task setting when the tasks are dependent
    on each other even though the data for them can be i.i.d. In both cases we model
    the data (samples or tasks) as stochastic processes and introduce new algorithms
    for both settings that take into account and exploit the resulting dependencies.
    We prove the theoretical guarantees on the performance of the introduced algorithms
    under different evaluation criteria and, in addition, we compliment the theoretical
    study by the empirical one, where we evaluate some of the algorithms on two real
    world datasets to highlight their practical applicability.
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Alexander
  full_name: Zimin, Alexander
  id: 37099E9C-F248-11E8-B48F-1D18A9856A87
  last_name: Zimin
citation:
  ama: Zimin A. Learning from dependent data. 2018. doi:<a href="https://doi.org/10.15479/AT:ISTA:TH1048">10.15479/AT:ISTA:TH1048</a>
  apa: Zimin, A. (2018). <i>Learning from dependent data</i>. Institute of Science
    and Technology Austria. <a href="https://doi.org/10.15479/AT:ISTA:TH1048">https://doi.org/10.15479/AT:ISTA:TH1048</a>
  chicago: Zimin, Alexander. “Learning from Dependent Data.” Institute of Science
    and Technology Austria, 2018. <a href="https://doi.org/10.15479/AT:ISTA:TH1048">https://doi.org/10.15479/AT:ISTA:TH1048</a>.
  ieee: A. Zimin, “Learning from dependent data,” Institute of Science and Technology
    Austria, 2018.
  ista: Zimin A. 2018. Learning from dependent data. Institute of Science and Technology
    Austria.
  mla: Zimin, Alexander. <i>Learning from Dependent Data</i>. Institute of Science
    and Technology Austria, 2018, doi:<a href="https://doi.org/10.15479/AT:ISTA:TH1048">10.15479/AT:ISTA:TH1048</a>.
  short: A. Zimin, Learning from Dependent Data, Institute of Science and Technology
    Austria, 2018.
date_created: 2018-12-11T11:44:27Z
date_published: 2018-09-01T00:00:00Z
date_updated: 2023-09-07T12:29:07Z
day: '01'
ddc:
- '004'
- '519'
degree_awarded: PhD
department:
- _id: ChLa
doi: 10.15479/AT:ISTA:TH1048
ec_funded: 1
file:
- access_level: open_access
  checksum: e849dd40a915e4d6c5572b51b517f098
  content_type: application/pdf
  creator: dernst
  date_created: 2019-04-09T07:32:47Z
  date_updated: 2020-07-14T12:47:40Z
  file_id: '6253'
  file_name: 2018_Thesis_Zimin.pdf
  file_size: 1036137
  relation: main_file
- access_level: closed
  checksum: da092153cec55c97461bd53c45c5d139
  content_type: application/zip
  creator: dernst
  date_created: 2019-04-09T07:32:47Z
  date_updated: 2020-07-14T12:47:40Z
  file_id: '6254'
  file_name: 2018_Thesis_Zimin_Source.zip
  file_size: 637490
  relation: source_file
file_date_updated: 2020-07-14T12:47:40Z
has_accepted_license: '1'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: '92'
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
  call_identifier: FP7
  grant_number: '308036'
  name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
  issn:
  - 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
publist_id: '7986'
pubrep_id: '1048'
status: public
supervisor:
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
title: Learning from dependent data
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2018'
...
---
_id: '1108'
abstract:
- lang: eng
  text: In this work we study the learnability of stochastic processes with respect
    to the conditional risk, i.e. the existence of a learning algorithm that improves
    its next-step performance with the amount of observed data. We introduce a notion
    of pairwise discrepancy between conditional distributions at different times steps
    and show how certain properties of these discrepancies can be used to construct
    a successful learning algorithm. Our main results are two theorems that establish
    criteria for learnability for many classes of stochastic processes, including
    all special cases studied previously in the literature.
alternative_title:
- PMLR
article_processing_charge: No
author:
- first_name: Alexander
  full_name: Zimin, Alexander
  id: 37099E9C-F248-11E8-B48F-1D18A9856A87
  last_name: Zimin
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
citation:
  ama: 'Zimin A, Lampert C. Learning theory for conditional risk minimization. In:
    Vol 54. ML Research Press; 2017:213-222.'
  apa: 'Zimin, A., &#38; Lampert, C. (2017). Learning theory for conditional risk
    minimization (Vol. 54, pp. 213–222). Presented at the AISTATS: Artificial Intelligence
    and Statistics, Fort Lauderdale, FL, United States: ML Research Press.'
  chicago: Zimin, Alexander, and Christoph Lampert. “Learning Theory for Conditional
    Risk Minimization,” 54:213–22. ML Research Press, 2017.
  ieee: 'A. Zimin and C. Lampert, “Learning theory for conditional risk minimization,”
    presented at the AISTATS: Artificial Intelligence and Statistics, Fort Lauderdale,
    FL, United States, 2017, vol. 54, pp. 213–222.'
  ista: 'Zimin A, Lampert C. 2017. Learning theory for conditional risk minimization.
    AISTATS: Artificial Intelligence and Statistics, PMLR, vol. 54, 213–222.'
  mla: Zimin, Alexander, and Christoph Lampert. <i>Learning Theory for Conditional
    Risk Minimization</i>. Vol. 54, ML Research Press, 2017, pp. 213–22.
  short: A. Zimin, C. Lampert, in:, ML Research Press, 2017, pp. 213–222.
conference:
  end_date: 2017-04-22
  location: Fort Lauderdale, FL, United States
  name: 'AISTATS: Artificial Intelligence and Statistics'
  start_date: 2017-04-20
date_created: 2018-12-11T11:50:11Z
date_published: 2017-04-01T00:00:00Z
date_updated: 2023-10-17T10:01:12Z
day: '01'
department:
- _id: ChLa
ec_funded: 1
external_id:
  isi:
  - '000509368500024'
intvolume: '        54'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: http://proceedings.mlr.press/v54/zimin17a/zimin17a.pdf
month: '04'
oa: 1
oa_version: Submitted Version
page: 213 - 222
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
  call_identifier: FP7
  grant_number: '308036'
  name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: ML Research Press
publist_id: '6261'
quality_controlled: '1'
status: public
title: Learning theory for conditional risk minimization
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 54
year: '2017'
...