--- _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' ...