---
_id: '6725'
abstract:
- lang: eng
text: "A Valued Constraint Satisfaction Problem (VCSP) provides a common framework
that can express a wide range of discrete optimization problems. A VCSP instance
is given by a finite set of variables, a finite domain of labels, and an objective
function to be minimized. This function is represented as a sum of terms where
each term depends on a subset of the variables. To obtain different classes of
optimization problems, one can restrict all terms to come from a fixed set Γ of
cost functions, called a language. \r\nRecent breakthrough results have established
a complete complexity classification of such classes with respect to language
Γ: if all cost functions in Γ satisfy a certain algebraic condition then all Γ-instances
can be solved in polynomial time, otherwise the problem is NP-hard. Unfortunately,
testing this condition for a given language Γ is known to be NP-hard. We thus
study exponential algorithms for this meta-problem. We show that the tractability
condition of a finite-valued language Γ can be tested in O(3‾√3|D|⋅poly(size(Γ)))
time, where D is the domain of Γ and poly(⋅) is some fixed polynomial. We also
obtain a matching lower bound under the Strong Exponential Time Hypothesis (SETH).
More precisely, we prove that for any constant δ<1 there is no O(3‾√3δ|D|) algorithm,
assuming that SETH holds."
alternative_title:
- LIPIcs
author:
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
citation:
ama: 'Kolmogorov V. Testing the complexity of a valued CSP language. In: 46th
International Colloquium on Automata, Languages and Programming. Vol 132.
Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2019:77:1-77:12. doi:10.4230/LIPICS.ICALP.2019.77'
apa: 'Kolmogorov, V. (2019). Testing the complexity of a valued CSP language. In
46th International Colloquium on Automata, Languages and Programming (Vol.
132, p. 77:1-77:12). Patras, Greece: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.
https://doi.org/10.4230/LIPICS.ICALP.2019.77'
chicago: Kolmogorov, Vladimir. “Testing the Complexity of a Valued CSP Language.”
In 46th International Colloquium on Automata, Languages and Programming,
132:77:1-77:12. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2019. https://doi.org/10.4230/LIPICS.ICALP.2019.77.
ieee: V. Kolmogorov, “Testing the complexity of a valued CSP language,” in 46th
International Colloquium on Automata, Languages and Programming, Patras, Greece,
2019, vol. 132, p. 77:1-77:12.
ista: 'Kolmogorov V. 2019. Testing the complexity of a valued CSP language. 46th
International Colloquium on Automata, Languages and Programming. ICALP 2019: International
Colloquim on Automata, Languages and Programming, LIPIcs, vol. 132, 77:1-77:12.'
mla: Kolmogorov, Vladimir. “Testing the Complexity of a Valued CSP Language.” 46th
International Colloquium on Automata, Languages and Programming, vol. 132,
Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2019, p. 77:1-77:12, doi:10.4230/LIPICS.ICALP.2019.77.
short: V. Kolmogorov, in:, 46th International Colloquium on Automata, Languages
and Programming, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2019, p. 77:1-77:12.
conference:
end_date: 2019-07-12
location: Patras, Greece
name: 'ICALP 2019: International Colloquim on Automata, Languages and Programming'
start_date: 2019-07-08
date_created: 2019-07-29T12:23:29Z
date_published: 2019-07-01T00:00:00Z
date_updated: 2021-01-12T08:08:40Z
day: '01'
ddc:
- '000'
department:
- _id: VlKo
doi: 10.4230/LIPICS.ICALP.2019.77
ec_funded: 1
external_id:
arxiv:
- '1803.02289'
file:
- access_level: open_access
checksum: f5ebee8eec6ae09e30365578ee63a492
content_type: application/pdf
creator: dernst
date_created: 2019-07-31T07:01:45Z
date_updated: 2020-07-14T12:47:38Z
file_id: '6738'
file_name: 2019_LIPICS_Kolmogorov.pdf
file_size: 575475
relation: main_file
file_date_updated: 2020-07-14T12:47:38Z
has_accepted_license: '1'
intvolume: ' 132'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 77:1-77:12
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication: 46th International Colloquium on Automata, Languages and Programming
publication_identifier:
isbn:
- 978-3-95977-109-2
issn:
- 1868-8969
publication_status: published
publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik
quality_controlled: '1'
scopus_import: 1
status: public
title: Testing the complexity of a valued CSP language
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 132
year: '2019'
...
---
_id: '6596'
abstract:
- lang: eng
text: It is well known that many problems in image recovery, signal processing,
and machine learning can be modeled as finding zeros of the sum of maximal monotone
and Lipschitz continuous monotone operators. Many papers have studied forward-backward
splitting methods for finding zeros of the sum of two monotone operators in Hilbert
spaces. Most of the proposed splitting methods in the literature have been proposed
for the sum of maximal monotone and inverse-strongly monotone operators in Hilbert
spaces. In this paper, we consider splitting methods for finding zeros of the
sum of maximal monotone operators and Lipschitz continuous monotone operators
in Banach spaces. We obtain weak and strong convergence results for the zeros
of the sum of maximal monotone and Lipschitz continuous monotone operators in
Banach spaces. Many already studied problems in the literature can be considered
as special cases of this paper.
article_number: '138'
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Yekini
full_name: Shehu, Yekini
id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87
last_name: Shehu
orcid: 0000-0001-9224-7139
citation:
ama: Shehu Y. Convergence results of forward-backward algorithms for sum of monotone
operators in Banach spaces. Results in Mathematics. 2019;74(4). doi:10.1007/s00025-019-1061-4
apa: Shehu, Y. (2019). Convergence results of forward-backward algorithms for sum
of monotone operators in Banach spaces. Results in Mathematics. Springer.
https://doi.org/10.1007/s00025-019-1061-4
chicago: Shehu, Yekini. “Convergence Results of Forward-Backward Algorithms for
Sum of Monotone Operators in Banach Spaces.” Results in Mathematics. Springer,
2019. https://doi.org/10.1007/s00025-019-1061-4.
ieee: Y. Shehu, “Convergence results of forward-backward algorithms for sum of monotone
operators in Banach spaces,” Results in Mathematics, vol. 74, no. 4. Springer,
2019.
ista: Shehu Y. 2019. Convergence results of forward-backward algorithms for sum
of monotone operators in Banach spaces. Results in Mathematics. 74(4), 138.
mla: Shehu, Yekini. “Convergence Results of Forward-Backward Algorithms for Sum
of Monotone Operators in Banach Spaces.” Results in Mathematics, vol. 74,
no. 4, 138, Springer, 2019, doi:10.1007/s00025-019-1061-4.
short: Y. Shehu, Results in Mathematics 74 (2019).
date_created: 2019-06-29T10:11:30Z
date_published: 2019-12-01T00:00:00Z
date_updated: 2023-08-28T12:26:22Z
day: '01'
ddc:
- '000'
department:
- _id: VlKo
doi: 10.1007/s00025-019-1061-4
ec_funded: 1
external_id:
arxiv:
- '2101.09068'
isi:
- '000473237500002'
file:
- access_level: open_access
checksum: c6d18cb1e16fc0c36a0e0f30b4ebbc2d
content_type: application/pdf
creator: kschuh
date_created: 2019-07-03T15:20:40Z
date_updated: 2020-07-14T12:47:34Z
file_id: '6605'
file_name: Springer_2019_Shehu.pdf
file_size: 466942
relation: main_file
file_date_updated: 2020-07-14T12:47:34Z
has_accepted_license: '1'
intvolume: ' 74'
isi: 1
issue: '4'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
- _id: B67AFEDC-15C9-11EA-A837-991A96BB2854
name: IST Austria Open Access Fund
publication: Results in Mathematics
publication_identifier:
eissn:
- 1420-9012
issn:
- 1422-6383
publication_status: published
publisher: Springer
quality_controlled: '1'
scopus_import: '1'
status: public
title: Convergence results of forward-backward algorithms for sum of monotone operators
in Banach spaces
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 74
year: '2019'
...
---
_id: '7000'
abstract:
- lang: eng
text: The main contributions of this paper are the proposition and the convergence
analysis of a class of inertial projection-type algorithm for solving variational
inequality problems in real Hilbert spaces where the underline operator is monotone
and uniformly continuous. We carry out a unified analysis of the proposed method
under very mild assumptions. In particular, weak convergence of the generated
sequence is established and nonasymptotic O(1 / n) rate of convergence is established,
where n denotes the iteration counter. We also present some experimental results
to illustrate the profits gained by introducing the inertial extrapolation steps.
article_number: '161'
article_processing_charge: No
article_type: original
author:
- first_name: Yekini
full_name: Shehu, Yekini
id: 3FC7CB58-F248-11E8-B48F-1D18A9856A87
last_name: Shehu
orcid: 0000-0001-9224-7139
- first_name: Olaniyi S.
full_name: Iyiola, Olaniyi S.
last_name: Iyiola
- first_name: Xiao-Huan
full_name: Li, Xiao-Huan
last_name: Li
- first_name: Qiao-Li
full_name: Dong, Qiao-Li
last_name: Dong
citation:
ama: Shehu Y, Iyiola OS, Li X-H, Dong Q-L. Convergence analysis of projection method
for variational inequalities. Computational and Applied Mathematics. 2019;38(4).
doi:10.1007/s40314-019-0955-9
apa: Shehu, Y., Iyiola, O. S., Li, X.-H., & Dong, Q.-L. (2019). Convergence
analysis of projection method for variational inequalities. Computational and
Applied Mathematics. Springer Nature. https://doi.org/10.1007/s40314-019-0955-9
chicago: Shehu, Yekini, Olaniyi S. Iyiola, Xiao-Huan Li, and Qiao-Li Dong. “Convergence
Analysis of Projection Method for Variational Inequalities.” Computational
and Applied Mathematics. Springer Nature, 2019. https://doi.org/10.1007/s40314-019-0955-9.
ieee: Y. Shehu, O. S. Iyiola, X.-H. Li, and Q.-L. Dong, “Convergence analysis of
projection method for variational inequalities,” Computational and Applied
Mathematics, vol. 38, no. 4. Springer Nature, 2019.
ista: Shehu Y, Iyiola OS, Li X-H, Dong Q-L. 2019. Convergence analysis of projection
method for variational inequalities. Computational and Applied Mathematics. 38(4),
161.
mla: Shehu, Yekini, et al. “Convergence Analysis of Projection Method for Variational
Inequalities.” Computational and Applied Mathematics, vol. 38, no. 4, 161,
Springer Nature, 2019, doi:10.1007/s40314-019-0955-9.
short: Y. Shehu, O.S. Iyiola, X.-H. Li, Q.-L. Dong, Computational and Applied Mathematics
38 (2019).
date_created: 2019-11-12T12:41:44Z
date_published: 2019-12-01T00:00:00Z
date_updated: 2023-08-30T07:20:32Z
day: '01'
ddc:
- '510'
- '515'
- '518'
department:
- _id: VlKo
doi: 10.1007/s40314-019-0955-9
ec_funded: 1
external_id:
arxiv:
- '2101.09081'
isi:
- '000488973100005'
has_accepted_license: '1'
intvolume: ' 38'
isi: 1
issue: '4'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1007/s40314-019-0955-9
month: '12'
oa: 1
oa_version: Published Version
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication: Computational and Applied Mathematics
publication_identifier:
eissn:
- 1807-0302
issn:
- 2238-3603
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Convergence analysis of projection method for variational inequalities
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 38
year: '2019'
...
---
_id: '7412'
abstract:
- lang: eng
text: We develop a framework for the rigorous analysis of focused stochastic local
search algorithms. These algorithms search a state space by repeatedly selecting
some constraint that is violated in the current state and moving to a random nearby
state that addresses the violation, while (we hope) not introducing many new violations.
An important class of focused local search algorithms with provable performance
guarantees has recently arisen from algorithmizations of the Lovász local lemma
(LLL), a nonconstructive tool for proving the existence of satisfying states by
introducing a background measure on the state space. While powerful, the state
transitions of algorithms in this class must be, in a precise sense, perfectly
compatible with the background measure. In many applications this is a very restrictive
requirement, and one needs to step outside the class. Here we introduce the notion
of measure distortion and develop a framework for analyzing arbitrary focused
stochastic local search algorithms, recovering LLL algorithmizations as the special
case of no distortion. Our framework takes as input an arbitrary algorithm of
such type and an arbitrary probability measure and shows how to use the measure
as a yardstick of algorithmic progress, even for algorithms designed independently
of the measure.
article_processing_charge: No
article_type: original
author:
- first_name: Dimitris
full_name: Achlioptas, Dimitris
last_name: Achlioptas
- first_name: Fotis
full_name: Iliopoulos, Fotis
last_name: Iliopoulos
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
citation:
ama: Achlioptas D, Iliopoulos F, Kolmogorov V. A local lemma for focused stochastical
algorithms. SIAM Journal on Computing. 2019;48(5):1583-1602. doi:10.1137/16m109332x
apa: Achlioptas, D., Iliopoulos, F., & Kolmogorov, V. (2019). A local lemma
for focused stochastical algorithms. SIAM Journal on Computing. SIAM. https://doi.org/10.1137/16m109332x
chicago: Achlioptas, Dimitris, Fotis Iliopoulos, and Vladimir Kolmogorov. “A Local
Lemma for Focused Stochastical Algorithms.” SIAM Journal on Computing.
SIAM, 2019. https://doi.org/10.1137/16m109332x.
ieee: D. Achlioptas, F. Iliopoulos, and V. Kolmogorov, “A local lemma for focused
stochastical algorithms,” SIAM Journal on Computing, vol. 48, no. 5. SIAM,
pp. 1583–1602, 2019.
ista: Achlioptas D, Iliopoulos F, Kolmogorov V. 2019. A local lemma for focused
stochastical algorithms. SIAM Journal on Computing. 48(5), 1583–1602.
mla: Achlioptas, Dimitris, et al. “A Local Lemma for Focused Stochastical Algorithms.”
SIAM Journal on Computing, vol. 48, no. 5, SIAM, 2019, pp. 1583–602, doi:10.1137/16m109332x.
short: D. Achlioptas, F. Iliopoulos, V. Kolmogorov, SIAM Journal on Computing 48
(2019) 1583–1602.
date_created: 2020-01-30T09:27:32Z
date_published: 2019-10-31T00:00:00Z
date_updated: 2023-09-06T15:25:29Z
day: '31'
department:
- _id: VlKo
doi: 10.1137/16m109332x
ec_funded: 1
external_id:
arxiv:
- '1809.01537'
isi:
- '000493900200005'
intvolume: ' 48'
isi: 1
issue: '5'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1809.01537
month: '10'
oa: 1
oa_version: Preprint
page: 1583-1602
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication: SIAM Journal on Computing
publication_identifier:
eissn:
- 1095-7111
issn:
- 0097-5397
publication_status: published
publisher: SIAM
quality_controlled: '1'
scopus_import: '1'
status: public
title: A local lemma for focused stochastical algorithms
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 48
year: '2019'
...
---
_id: '7468'
abstract:
- lang: eng
text: We present a new proximal bundle method for Maximum-A-Posteriori (MAP) inference
in structured energy minimization problems. The method optimizes a Lagrangean
relaxation of the original energy minimization problem using a multi plane block-coordinate
Frank-Wolfe method that takes advantage of the specific structure of the Lagrangean
decomposition. We show empirically that our method outperforms state-of-the-art
Lagrangean decomposition based algorithms on some challenging Markov Random Field,
multi-label discrete tomography and graph matching problems.
article_number: 11138-11147
article_processing_charge: No
author:
- first_name: Paul
full_name: Swoboda, Paul
id: 446560C6-F248-11E8-B48F-1D18A9856A87
last_name: Swoboda
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
citation:
ama: 'Swoboda P, Kolmogorov V. Map inference via block-coordinate Frank-Wolfe algorithm.
In: Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition. Vol 2019-June. IEEE; 2019. doi:10.1109/CVPR.2019.01140'
apa: 'Swoboda, P., & Kolmogorov, V. (2019). Map inference via block-coordinate
Frank-Wolfe algorithm. In Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition (Vol. 2019–June). Long Beach, CA,
United States: IEEE. https://doi.org/10.1109/CVPR.2019.01140'
chicago: Swoboda, Paul, and Vladimir Kolmogorov. “Map Inference via Block-Coordinate
Frank-Wolfe Algorithm.” In Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition, Vol. 2019–June. IEEE, 2019. https://doi.org/10.1109/CVPR.2019.01140.
ieee: P. Swoboda and V. Kolmogorov, “Map inference via block-coordinate Frank-Wolfe
algorithm,” in Proceedings of the IEEE Computer Society Conference on Computer
Vision and Pattern Recognition, Long Beach, CA, United States, 2019, vol.
2019–June.
ista: 'Swoboda P, Kolmogorov V. 2019. Map inference via block-coordinate Frank-Wolfe
algorithm. Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition
vol. 2019–June, 11138–11147.'
mla: Swoboda, Paul, and Vladimir Kolmogorov. “Map Inference via Block-Coordinate
Frank-Wolfe Algorithm.” Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition, vol. 2019–June, 11138–11147, IEEE,
2019, doi:10.1109/CVPR.2019.01140.
short: P. Swoboda, V. Kolmogorov, in:, Proceedings of the IEEE Computer Society
Conference on Computer Vision and Pattern Recognition, IEEE, 2019.
conference:
end_date: 2019-06-20
location: Long Beach, CA, United States
name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
start_date: 2019-06-15
date_created: 2020-02-09T23:00:52Z
date_published: 2019-06-01T00:00:00Z
date_updated: 2023-09-07T14:54:24Z
day: '01'
department:
- _id: VlKo
doi: 10.1109/CVPR.2019.01140
ec_funded: 1
external_id:
arxiv:
- '1806.05049'
isi:
- '000542649304076'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1806.05049
month: '06'
oa: 1
oa_version: Preprint
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication: Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition
publication_identifier:
isbn:
- '9781728132938'
issn:
- '10636919'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Map inference via block-coordinate Frank-Wolfe algorithm
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 2019-June
year: '2019'
...
---
_id: '7639'
abstract:
- lang: eng
text: Deep neural networks (DNNs) have become increasingly important due to their
excellent empirical performance on a wide range of problems. However, regularization
is generally achieved by indirect means, largely due to the complex set of functions
defined by a network and the difficulty in measuring function complexity. There
exists no method in the literature for additive regularization based on a norm
of the function, as is classically considered in statistical learning theory.
In this work, we study the tractability of function norms for deep neural networks
with ReLU activations. We provide, to the best of our knowledge, the first proof
in the literature of the NP-hardness of computing function norms of DNNs of 3
or more layers. We also highlight a fundamental difference between shallow and
deep networks. In the light on these results, we propose a new regularization
strategy based on approximate function norms, and show its efficiency on a segmentation
task with a DNN.
article_number: 748-752
article_processing_charge: No
author:
- first_name: Amal
full_name: Rannen-Triki, Amal
last_name: Rannen-Triki
- first_name: Maxim
full_name: Berman, Maxim
last_name: Berman
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
- first_name: Matthew B.
full_name: Blaschko, Matthew B.
last_name: Blaschko
citation:
ama: 'Rannen-Triki A, Berman M, Kolmogorov V, Blaschko MB. Function norms for neural
networks. In: Proceedings of the 2019 International Conference on Computer
Vision Workshop. IEEE; 2019. doi:10.1109/ICCVW.2019.00097'
apa: 'Rannen-Triki, A., Berman, M., Kolmogorov, V., & Blaschko, M. B. (2019).
Function norms for neural networks. In Proceedings of the 2019 International
Conference on Computer Vision Workshop. Seoul, South Korea: IEEE. https://doi.org/10.1109/ICCVW.2019.00097'
chicago: Rannen-Triki, Amal, Maxim Berman, Vladimir Kolmogorov, and Matthew B. Blaschko.
“Function Norms for Neural Networks.” In Proceedings of the 2019 International
Conference on Computer Vision Workshop. IEEE, 2019. https://doi.org/10.1109/ICCVW.2019.00097.
ieee: A. Rannen-Triki, M. Berman, V. Kolmogorov, and M. B. Blaschko, “Function norms
for neural networks,” in Proceedings of the 2019 International Conference on
Computer Vision Workshop, Seoul, South Korea, 2019.
ista: 'Rannen-Triki A, Berman M, Kolmogorov V, Blaschko MB. 2019. Function norms
for neural networks. Proceedings of the 2019 International Conference on Computer
Vision Workshop. ICCVW: International Conference on Computer Vision Workshop,
748–752.'
mla: Rannen-Triki, Amal, et al. “Function Norms for Neural Networks.” Proceedings
of the 2019 International Conference on Computer Vision Workshop, 748–752,
IEEE, 2019, doi:10.1109/ICCVW.2019.00097.
short: A. Rannen-Triki, M. Berman, V. Kolmogorov, M.B. Blaschko, in:, Proceedings
of the 2019 International Conference on Computer Vision Workshop, IEEE, 2019.
conference:
end_date: 2019-10-28
location: Seoul, South Korea
name: 'ICCVW: International Conference on Computer Vision Workshop'
start_date: 2019-10-27
date_created: 2020-04-05T22:00:50Z
date_published: 2019-10-01T00:00:00Z
date_updated: 2023-09-08T11:19:12Z
day: '01'
department:
- _id: VlKo
doi: 10.1109/ICCVW.2019.00097
external_id:
isi:
- '000554591600090'
isi: 1
language:
- iso: eng
month: '10'
oa_version: None
publication: Proceedings of the 2019 International Conference on Computer Vision Workshop
publication_identifier:
isbn:
- '9781728150239'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Function norms for neural networks
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2019'
...
---
_id: '703'
abstract:
- lang: eng
text: We consider the NP-hard problem of MAP-inference for undirected discrete graphical
models. We propose a polynomial time and practically efficient algorithm for finding
a part of its optimal solution. Specifically, our algorithm marks some labels
of the considered graphical model either as (i) optimal, meaning that they belong
to all optimal solutions of the inference problem; (ii) non-optimal if they provably
do not belong to any solution. With access to an exact solver of a linear programming
relaxation to the MAP-inference problem, our algorithm marks the maximal possible
(in a specified sense) number of labels. We also present a version of the algorithm,
which has access to a suboptimal dual solver only and still can ensure the (non-)optimality
for the marked labels, although the overall number of the marked labels may decrease.
We propose an efficient implementation, which runs in time comparable to a single
run of a suboptimal dual solver. Our method is well-scalable and shows state-of-the-art
results on computational benchmarks from machine learning and computer vision.
author:
- first_name: Alexander
full_name: Shekhovtsov, Alexander
last_name: Shekhovtsov
- first_name: Paul
full_name: Swoboda, Paul
id: 446560C6-F248-11E8-B48F-1D18A9856A87
last_name: Swoboda
- first_name: Bogdan
full_name: Savchynskyy, Bogdan
last_name: Savchynskyy
citation:
ama: Shekhovtsov A, Swoboda P, Savchynskyy B. Maximum persistency via iterative
relaxed inference with graphical models. IEEE Transactions on Pattern Analysis
and Machine Intelligence. 2018;40(7):1668-1682. doi:10.1109/TPAMI.2017.2730884
apa: Shekhovtsov, A., Swoboda, P., & Savchynskyy, B. (2018). Maximum persistency
via iterative relaxed inference with graphical models. IEEE Transactions on
Pattern Analysis and Machine Intelligence. IEEE. https://doi.org/10.1109/TPAMI.2017.2730884
chicago: Shekhovtsov, Alexander, Paul Swoboda, and Bogdan Savchynskyy. “Maximum
Persistency via Iterative Relaxed Inference with Graphical Models.” IEEE Transactions
on Pattern Analysis and Machine Intelligence. IEEE, 2018. https://doi.org/10.1109/TPAMI.2017.2730884.
ieee: A. Shekhovtsov, P. Swoboda, and B. Savchynskyy, “Maximum persistency via iterative
relaxed inference with graphical models,” IEEE Transactions on Pattern Analysis
and Machine Intelligence, vol. 40, no. 7. IEEE, pp. 1668–1682, 2018.
ista: Shekhovtsov A, Swoboda P, Savchynskyy B. 2018. Maximum persistency via iterative
relaxed inference with graphical models. IEEE Transactions on Pattern Analysis
and Machine Intelligence. 40(7), 1668–1682.
mla: Shekhovtsov, Alexander, et al. “Maximum Persistency via Iterative Relaxed Inference
with Graphical Models.” IEEE Transactions on Pattern Analysis and Machine Intelligence,
vol. 40, no. 7, IEEE, 2018, pp. 1668–82, doi:10.1109/TPAMI.2017.2730884.
short: A. Shekhovtsov, P. Swoboda, B. Savchynskyy, IEEE Transactions on Pattern
Analysis and Machine Intelligence 40 (2018) 1668–1682.
date_created: 2018-12-11T11:48:01Z
date_published: 2018-07-01T00:00:00Z
date_updated: 2021-01-12T08:11:32Z
day: '01'
department:
- _id: VlKo
doi: 10.1109/TPAMI.2017.2730884
external_id:
arxiv:
- '1508.07902'
intvolume: ' 40'
issue: '7'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1508.07902
month: '07'
oa: 1
oa_version: Preprint
page: 1668-1682
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_identifier:
issn:
- '01628828'
publication_status: published
publisher: IEEE
publist_id: '6992'
quality_controlled: '1'
scopus_import: 1
status: public
title: Maximum persistency via iterative relaxed inference with graphical models
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 40
year: '2018'
...
---
_id: '10864'
abstract:
- lang: eng
text: We prove that every congruence distributive variety has directed Jónsson terms,
and every congruence modular variety has directed Gumm terms. The directed terms
we construct witness every case of absorption witnessed by the original Jónsson
or Gumm terms. This result is equivalent to a pair of claims about absorption
for admissible preorders in congruence distributive and congruence modular varieties,
respectively. For finite algebras, these absorption theorems have already seen
significant applications, but until now, it was not clear if the theorems hold
for general algebras as well. Our method also yields a novel proof of a result
by P. Lipparini about the existence of a chain of terms (which we call Pixley
terms) in varieties that are at the same time congruence distributive and k-permutable
for some k.
acknowledgement: The second author was supported by National Science Center grant
DEC-2011-/01/B/ST6/01006.
article_processing_charge: No
author:
- first_name: Alexandr
full_name: Kazda, Alexandr
id: 3B32BAA8-F248-11E8-B48F-1D18A9856A87
last_name: Kazda
- first_name: Marcin
full_name: Kozik, Marcin
last_name: Kozik
- first_name: Ralph
full_name: McKenzie, Ralph
last_name: McKenzie
- first_name: Matthew
full_name: Moore, Matthew
last_name: Moore
citation:
ama: 'Kazda A, Kozik M, McKenzie R, Moore M. Absorption and directed Jónsson terms.
In: Czelakowski J, ed. Don Pigozzi on Abstract Algebraic Logic, Universal Algebra,
and Computer Science. Vol 16. OCTR. Cham: Springer Nature; 2018:203-220. doi:10.1007/978-3-319-74772-9_7'
apa: 'Kazda, A., Kozik, M., McKenzie, R., & Moore, M. (2018). Absorption and
directed Jónsson terms. In J. Czelakowski (Ed.), Don Pigozzi on Abstract Algebraic
Logic, Universal Algebra, and Computer Science (Vol. 16, pp. 203–220). Cham:
Springer Nature. https://doi.org/10.1007/978-3-319-74772-9_7'
chicago: 'Kazda, Alexandr, Marcin Kozik, Ralph McKenzie, and Matthew Moore. “Absorption
and Directed Jónsson Terms.” In Don Pigozzi on Abstract Algebraic Logic, Universal
Algebra, and Computer Science, edited by J Czelakowski, 16:203–20. OCTR. Cham:
Springer Nature, 2018. https://doi.org/10.1007/978-3-319-74772-9_7.'
ieee: 'A. Kazda, M. Kozik, R. McKenzie, and M. Moore, “Absorption and directed Jónsson
terms,” in Don Pigozzi on Abstract Algebraic Logic, Universal Algebra, and
Computer Science, vol. 16, J. Czelakowski, Ed. Cham: Springer Nature, 2018,
pp. 203–220.'
ista: 'Kazda A, Kozik M, McKenzie R, Moore M. 2018.Absorption and directed Jónsson
terms. In: Don Pigozzi on Abstract Algebraic Logic, Universal Algebra, and Computer
Science. vol. 16, 203–220.'
mla: Kazda, Alexandr, et al. “Absorption and Directed Jónsson Terms.” Don Pigozzi
on Abstract Algebraic Logic, Universal Algebra, and Computer Science, edited
by J Czelakowski, vol. 16, Springer Nature, 2018, pp. 203–20, doi:10.1007/978-3-319-74772-9_7.
short: A. Kazda, M. Kozik, R. McKenzie, M. Moore, in:, J. Czelakowski (Ed.), Don
Pigozzi on Abstract Algebraic Logic, Universal Algebra, and Computer Science,
Springer Nature, Cham, 2018, pp. 203–220.
date_created: 2022-03-18T10:30:32Z
date_published: 2018-03-21T00:00:00Z
date_updated: 2023-09-05T15:37:18Z
day: '21'
department:
- _id: VlKo
doi: 10.1007/978-3-319-74772-9_7
editor:
- first_name: J
full_name: Czelakowski, J
last_name: Czelakowski
external_id:
arxiv:
- '1502.01072'
intvolume: ' 16'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1502.01072
month: '03'
oa: 1
oa_version: Preprint
page: 203-220
place: Cham
publication: Don Pigozzi on Abstract Algebraic Logic, Universal Algebra, and Computer
Science
publication_identifier:
eisbn:
- '9783319747729'
eissn:
- 2211-2766
isbn:
- '9783319747712'
issn:
- 2211-2758
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
series_title: OCTR
status: public
title: Absorption and directed Jónsson terms
type: book_chapter
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 16
year: '2018'
...
---
_id: '273'
abstract:
- lang: eng
text: The accuracy of information retrieval systems is often measured using complex
loss functions such as the average precision (AP) or the normalized discounted
cumulative gain (NDCG). Given a set of positive and negative samples, the parameters
of a retrieval system can be estimated by minimizing these loss functions. However,
the non-differentiability and non-decomposability of these loss functions does
not allow for simple gradient based optimization algorithms. This issue is generally
circumvented by either optimizing a structured hinge-loss upper bound to the loss
function or by using asymptotic methods like the direct-loss minimization framework.
Yet, the high computational complexity of loss-augmented inference, which is necessary
for both the frameworks, prohibits its use in large training data sets. To alleviate
this deficiency, we present a novel quicksort flavored algorithm for a large class
of non-decomposable loss functions. We provide a complete characterization of
the loss functions that are amenable to our algorithm, and show that it includes
both AP and NDCG based loss functions. Furthermore, we prove that no comparison
based algorithm can improve upon the computational complexity of our approach
asymptotically. We demonstrate the effectiveness of our approach in the context
of optimizing the structured hinge loss upper bound of AP and NDCG loss for learning
models for a variety of vision tasks. We show that our approach provides significantly
better results than simpler decomposable loss functions, while requiring a comparable
training time.
article_processing_charge: No
author:
- first_name: Pritish
full_name: Mohapatra, Pritish
last_name: Mohapatra
- first_name: Michal
full_name: Rolinek, Michal
id: 3CB3BC06-F248-11E8-B48F-1D18A9856A87
last_name: Rolinek
- first_name: C V
full_name: Jawahar, C V
last_name: Jawahar
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
- first_name: M Pawan
full_name: Kumar, M Pawan
last_name: Kumar
citation:
ama: 'Mohapatra P, Rolinek M, Jawahar CV, Kolmogorov V, Kumar MP. Efficient optimization
for rank-based loss functions. In: 2018 IEEE/CVF Conference on Computer Vision
and Pattern Recognition. IEEE; 2018:3693-3701. doi:10.1109/cvpr.2018.00389'
apa: 'Mohapatra, P., Rolinek, M., Jawahar, C. V., Kolmogorov, V., & Kumar, M.
P. (2018). Efficient optimization for rank-based loss functions. In 2018 IEEE/CVF
Conference on Computer Vision and Pattern Recognition (pp. 3693–3701). Salt
Lake City, UT, USA: IEEE. https://doi.org/10.1109/cvpr.2018.00389'
chicago: Mohapatra, Pritish, Michal Rolinek, C V Jawahar, Vladimir Kolmogorov, and
M Pawan Kumar. “Efficient Optimization for Rank-Based Loss Functions.” In 2018
IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3693–3701.
IEEE, 2018. https://doi.org/10.1109/cvpr.2018.00389.
ieee: P. Mohapatra, M. Rolinek, C. V. Jawahar, V. Kolmogorov, and M. P. Kumar, “Efficient
optimization for rank-based loss functions,” in 2018 IEEE/CVF Conference on
Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp.
3693–3701.
ista: 'Mohapatra P, Rolinek M, Jawahar CV, Kolmogorov V, Kumar MP. 2018. Efficient
optimization for rank-based loss functions. 2018 IEEE/CVF Conference on Computer
Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern
Recognition, 3693–3701.'
mla: Mohapatra, Pritish, et al. “Efficient Optimization for Rank-Based Loss Functions.”
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE,
2018, pp. 3693–701, doi:10.1109/cvpr.2018.00389.
short: P. Mohapatra, M. Rolinek, C.V. Jawahar, V. Kolmogorov, M.P. Kumar, in:, 2018
IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018, pp.
3693–3701.
conference:
end_date: 2018-06-22
location: Salt Lake City, UT, USA
name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
start_date: 2018-06-18
date_created: 2018-12-11T11:45:33Z
date_published: 2018-06-28T00:00:00Z
date_updated: 2023-09-11T13:24:43Z
day: '28'
department:
- _id: VlKo
doi: 10.1109/cvpr.2018.00389
ec_funded: 1
external_id:
arxiv:
- '1604.08269'
isi:
- '000457843603087'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1604.08269
month: '06'
oa: 1
oa_version: Preprint
page: 3693-3701
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
publication_identifier:
isbn:
- '9781538664209'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Efficient optimization for rank-based loss functions
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2018'
...
---
_id: '193'
abstract:
- lang: eng
text: 'We show attacks on five data-independent memory-hard functions (iMHF) that
were submitted to the password hashing competition (PHC). Informally, an MHF is
a function which cannot be evaluated on dedicated hardware, like ASICs, at significantly
lower hardware and/or energy cost than evaluating a single instance on a standard
single-core architecture. Data-independent means the memory access pattern of
the function is independent of the input; this makes iMHFs harder to construct
than data-dependent ones, but the latter can be attacked by various side-channel
attacks. Following [Alwen-Blocki''16], we capture the evaluation of an iMHF as
a directed acyclic graph (DAG). The cumulative parallel pebbling complexity of
this DAG is a measure for the hardware cost of evaluating the iMHF on an ASIC.
Ideally, one would like the complexity of a DAG underlying an iMHF to be as close
to quadratic in the number of nodes of the graph as possible. Instead, we show
that (the DAGs underlying) the following iMHFs are far from this bound: Rig.v2,
TwoCats and Gambit each having an exponent no more than 1.75. Moreover, we show
that the complexity of the iMHF modes of the PHC finalists Pomelo and Lyra2 have
exponents at most 1.83 and 1.67 respectively. To show this we investigate a combinatorial
property of each underlying DAG (called its depth-robustness. By establishing
upper bounds on this property we are then able to apply the general technique
of [Alwen-Block''16] for analyzing the hardware costs of an iMHF.'
acknowledgement: Leonid Reyzin was supported in part by IST Austria and by US NSF
grants 1012910, 1012798, and 1422965; this research was performed while he was visiting
IST Austria.
article_processing_charge: No
author:
- first_name: Joel F
full_name: Alwen, Joel F
id: 2A8DFA8C-F248-11E8-B48F-1D18A9856A87
last_name: Alwen
- first_name: Peter
full_name: Gazi, Peter
last_name: Gazi
- first_name: Chethan
full_name: Kamath Hosdurg, Chethan
id: 4BD3F30E-F248-11E8-B48F-1D18A9856A87
last_name: Kamath Hosdurg
- first_name: Karen
full_name: Klein, Karen
id: 3E83A2F8-F248-11E8-B48F-1D18A9856A87
last_name: Klein
- first_name: Georg F
full_name: Osang, Georg F
id: 464B40D6-F248-11E8-B48F-1D18A9856A87
last_name: Osang
orcid: 0000-0002-8882-5116
- first_name: Krzysztof Z
full_name: Pietrzak, Krzysztof Z
id: 3E04A7AA-F248-11E8-B48F-1D18A9856A87
last_name: Pietrzak
orcid: 0000-0002-9139-1654
- first_name: Lenoid
full_name: Reyzin, Lenoid
last_name: Reyzin
- first_name: Michal
full_name: Rolinek, Michal
id: 3CB3BC06-F248-11E8-B48F-1D18A9856A87
last_name: Rolinek
- first_name: Michal
full_name: Rybar, Michal
id: 2B3E3DE8-F248-11E8-B48F-1D18A9856A87
last_name: Rybar
citation:
ama: 'Alwen JF, Gazi P, Kamath Hosdurg C, et al. On the memory hardness of data
independent password hashing functions. In: Proceedings of the 2018 on Asia
Conference on Computer and Communication Security. ACM; 2018:51-65. doi:10.1145/3196494.3196534'
apa: 'Alwen, J. F., Gazi, P., Kamath Hosdurg, C., Klein, K., Osang, G. F., Pietrzak,
K. Z., … Rybar, M. (2018). On the memory hardness of data independent password
hashing functions. In Proceedings of the 2018 on Asia Conference on Computer
and Communication Security (pp. 51–65). Incheon, Republic of Korea: ACM. https://doi.org/10.1145/3196494.3196534'
chicago: Alwen, Joel F, Peter Gazi, Chethan Kamath Hosdurg, Karen Klein, Georg F
Osang, Krzysztof Z Pietrzak, Lenoid Reyzin, Michal Rolinek, and Michal Rybar.
“On the Memory Hardness of Data Independent Password Hashing Functions.” In Proceedings
of the 2018 on Asia Conference on Computer and Communication Security, 51–65.
ACM, 2018. https://doi.org/10.1145/3196494.3196534.
ieee: J. F. Alwen et al., “On the memory hardness of data independent password
hashing functions,” in Proceedings of the 2018 on Asia Conference on Computer
and Communication Security, Incheon, Republic of Korea, 2018, pp. 51–65.
ista: 'Alwen JF, Gazi P, Kamath Hosdurg C, Klein K, Osang GF, Pietrzak KZ, Reyzin
L, Rolinek M, Rybar M. 2018. On the memory hardness of data independent password
hashing functions. Proceedings of the 2018 on Asia Conference on Computer and
Communication Security. ASIACCS: Asia Conference on Computer and Communications
Security , 51–65.'
mla: Alwen, Joel F., et al. “On the Memory Hardness of Data Independent Password
Hashing Functions.” Proceedings of the 2018 on Asia Conference on Computer
and Communication Security, ACM, 2018, pp. 51–65, doi:10.1145/3196494.3196534.
short: J.F. Alwen, P. Gazi, C. Kamath Hosdurg, K. Klein, G.F. Osang, K.Z. Pietrzak,
L. Reyzin, M. Rolinek, M. Rybar, in:, Proceedings of the 2018 on Asia Conference
on Computer and Communication Security, ACM, 2018, pp. 51–65.
conference:
end_date: 2018-06-08
location: Incheon, Republic of Korea
name: 'ASIACCS: Asia Conference on Computer and Communications Security '
start_date: 2018-06-04
date_created: 2018-12-11T11:45:07Z
date_published: 2018-06-01T00:00:00Z
date_updated: 2023-09-13T09:13:12Z
day: '01'
department:
- _id: KrPi
- _id: HeEd
- _id: VlKo
doi: 10.1145/3196494.3196534
ec_funded: 1
external_id:
isi:
- '000516620100005'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://eprint.iacr.org/2016/783
month: '06'
oa: 1
oa_version: Submitted Version
page: 51 - 65
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '616160'
name: 'Discrete Optimization in Computer Vision: Theory and Practice'
- _id: 258AA5B2-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '682815'
name: Teaching Old Crypto New Tricks
publication: Proceedings of the 2018 on Asia Conference on Computer and Communication
Security
publication_status: published
publisher: ACM
publist_id: '7723'
quality_controlled: '1'
scopus_import: '1'
status: public
title: On the memory hardness of data independent password hashing functions
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2018'
...