---
_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:10.15479/AT:ISTA:TH1048
apa: Zimin, A. (2018). Learning from dependent data. Institute of Science
and Technology Austria. https://doi.org/10.15479/AT:ISTA:TH1048
chicago: Zimin, Alexander. “Learning from Dependent Data.” Institute of Science
and Technology Austria, 2018. https://doi.org/10.15479/AT:ISTA:TH1048.
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. Learning from Dependent Data. Institute of Science
and Technology Austria, 2018, doi:10.15479/AT:ISTA:TH1048.
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'
...