--- _id: '641' abstract: - lang: eng text: 'We introduce two novel methods for learning parameters of graphical models for image labelling. The following two tasks underline both methods: (i) perturb model parameters based on given features and ground truth labelings, so as to exactly reproduce these labelings as optima of the local polytope relaxation of the labelling problem; (ii) train a predictor for the perturbed model parameters so that improved model parameters can be applied to the labelling of novel data. Our first method implements task (i) by inverse linear programming and task (ii) using a regressor e.g. a Gaussian process. Our second approach simultaneously solves tasks (i) and (ii) in a joint manner, while being restricted to linearly parameterised predictors. Experiments demonstrate the merits of both approaches.' alternative_title: - LNCS author: - first_name: Vera full_name: Trajkovska, Vera last_name: Trajkovska - first_name: Paul full_name: Swoboda, Paul id: 446560C6-F248-11E8-B48F-1D18A9856A87 last_name: Swoboda - first_name: Freddie full_name: Åström, Freddie last_name: Åström - first_name: Stefanie full_name: Petra, Stefanie last_name: Petra citation: ama: 'Trajkovska V, Swoboda P, Åström F, Petra S. Graphical model parameter learning by inverse linear programming. In: Lauze F, Dong Y, Bjorholm Dahl A, eds. Vol 10302. Springer; 2017:323-334. doi:10.1007/978-3-319-58771-4_26' apa: 'Trajkovska, V., Swoboda, P., Åström, F., & Petra, S. (2017). Graphical model parameter learning by inverse linear programming. In F. Lauze, Y. Dong, & A. Bjorholm Dahl (Eds.) (Vol. 10302, pp. 323–334). Presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark: Springer. https://doi.org/10.1007/978-3-319-58771-4_26' chicago: Trajkovska, Vera, Paul Swoboda, Freddie Åström, and Stefanie Petra. “Graphical Model Parameter Learning by Inverse Linear Programming.” edited by François Lauze, Yiqiu Dong, and Anders Bjorholm Dahl, 10302:323–34. Springer, 2017. https://doi.org/10.1007/978-3-319-58771-4_26. ieee: 'V. Trajkovska, P. Swoboda, F. Åström, and S. Petra, “Graphical model parameter learning by inverse linear programming,” presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark, 2017, vol. 10302, pp. 323–334.' ista: 'Trajkovska V, Swoboda P, Åström F, Petra S. 2017. Graphical model parameter learning by inverse linear programming. SSVM: Scale Space and Variational Methods in Computer Vision, LNCS, vol. 10302, 323–334.' mla: Trajkovska, Vera, et al. Graphical Model Parameter Learning by Inverse Linear Programming. Edited by François Lauze et al., vol. 10302, Springer, 2017, pp. 323–34, doi:10.1007/978-3-319-58771-4_26. short: V. Trajkovska, P. Swoboda, F. Åström, S. Petra, in:, F. Lauze, Y. Dong, A. Bjorholm Dahl (Eds.), Springer, 2017, pp. 323–334. conference: end_date: 2017-06-08 location: Kolding, Denmark name: 'SSVM: Scale Space and Variational Methods in Computer Vision' start_date: 2017-06-04 date_created: 2018-12-11T11:47:39Z date_published: 2017-01-01T00:00:00Z date_updated: 2021-01-12T08:07:23Z day: '01' department: - _id: VlKo doi: 10.1007/978-3-319-58771-4_26 editor: - first_name: François full_name: Lauze, François last_name: Lauze - first_name: Yiqiu full_name: Dong, Yiqiu last_name: Dong - first_name: Anders full_name: Bjorholm Dahl, Anders last_name: Bjorholm Dahl intvolume: ' 10302' language: - iso: eng month: '01' oa_version: None page: 323 - 334 publication_identifier: isbn: - 978-331958770-7 publication_status: published publisher: Springer publist_id: '7147' quality_controlled: '1' scopus_import: 1 status: public title: Graphical model parameter learning by inverse linear programming type: conference user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 10302 year: '2017' ... --- _id: '644' abstract: - lang: eng text: An instance of the valued constraint satisfaction problem (VCSP) is given by a finite set of variables, a finite domain of labels, and a sum of functions, each function depending on a subset of the variables. Each function can take finite values specifying costs of assignments of labels to its variables or the infinite value, which indicates an infeasible assignment. The goal is to find an assignment of labels to the variables that minimizes the sum. We study, assuming that P 6= NP, how the complexity of this very general problem depends on the set of functions allowed in the instances, the so-called constraint language. The case when all allowed functions take values in f0;1g corresponds to ordinary CSPs, where one deals only with the feasibility issue, and there is no optimization. This case is the subject of the algebraic CSP dichotomy conjecture predicting for which constraint languages CSPs are tractable (i.e., solvable in polynomial time) and for which they are NP-hard. The case when all allowed functions take only finite values corresponds to a finitevalued CSP, where the feasibility aspect is trivial and one deals only with the optimization issue. The complexity of finite-valued CSPs was fully classified by Thapper and Živný. An algebraic necessary condition for tractability of a general-valued CSP with a fixed constraint language was recently given by Kozik and Ochremiak. As our main result, we prove that if a constraint language satisfies this algebraic necessary condition, and the feasibility CSP (i.e., the problem of deciding whether a given instance has a feasible solution) corresponding to the VCSP with this language is tractable, then the VCSP is tractable. The algorithm is a simple combination of the assumed algorithm for the feasibility CSP and the standard LP relaxation. As a corollary, we obtain that a dichotomy for ordinary CSPs would imply a dichotomy for general-valued CSPs. author: - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov - first_name: Andrei full_name: Krokhin, Andrei last_name: Krokhin - first_name: Michal full_name: Rolinek, Michal id: 3CB3BC06-F248-11E8-B48F-1D18A9856A87 last_name: Rolinek citation: ama: Kolmogorov V, Krokhin A, Rolinek M. The complexity of general-valued CSPs. SIAM Journal on Computing. 2017;46(3):1087-1110. doi:10.1137/16M1091836 apa: Kolmogorov, V., Krokhin, A., & Rolinek, M. (2017). The complexity of general-valued CSPs. SIAM Journal on Computing. SIAM. https://doi.org/10.1137/16M1091836 chicago: Kolmogorov, Vladimir, Andrei Krokhin, and Michal Rolinek. “The Complexity of General-Valued CSPs.” SIAM Journal on Computing. SIAM, 2017. https://doi.org/10.1137/16M1091836. ieee: V. Kolmogorov, A. Krokhin, and M. Rolinek, “The complexity of general-valued CSPs,” SIAM Journal on Computing, vol. 46, no. 3. SIAM, pp. 1087–1110, 2017. ista: Kolmogorov V, Krokhin A, Rolinek M. 2017. The complexity of general-valued CSPs. SIAM Journal on Computing. 46(3), 1087–1110. mla: Kolmogorov, Vladimir, et al. “The Complexity of General-Valued CSPs.” SIAM Journal on Computing, vol. 46, no. 3, SIAM, 2017, pp. 1087–110, doi:10.1137/16M1091836. short: V. Kolmogorov, A. Krokhin, M. Rolinek, SIAM Journal on Computing 46 (2017) 1087–1110. date_created: 2018-12-11T11:47:40Z date_published: 2017-06-29T00:00:00Z date_updated: 2023-02-23T10:07:49Z day: '29' department: - _id: VlKo doi: 10.1137/16M1091836 ec_funded: 1 intvolume: ' 46' issue: '3' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1502.07327 month: '06' oa: 1 oa_version: Preprint page: 1087 - 1110 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_status: published publisher: SIAM publist_id: '7138' quality_controlled: '1' related_material: record: - id: '1637' relation: other status: public scopus_import: 1 status: public title: The complexity of general-valued CSPs type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 46 year: '2017' ... --- _id: '646' abstract: - lang: eng text: We present a novel convex relaxation and a corresponding inference algorithm for the non-binary discrete tomography problem, that is, reconstructing discrete-valued images from few linear measurements. In contrast to state of the art approaches that split the problem into a continuous reconstruction problem for the linear measurement constraints and a discrete labeling problem to enforce discrete-valued reconstructions, we propose a joint formulation that addresses both problems simultaneously, resulting in a tighter convex relaxation. For this purpose a constrained graphical model is set up and evaluated using a novel relaxation optimized by dual decomposition. We evaluate our approach experimentally and show superior solutions both mathematically (tighter relaxation) and experimentally in comparison to previously proposed relaxations. alternative_title: - LNCS author: - first_name: Jan full_name: Kuske, Jan last_name: Kuske - first_name: Paul full_name: Swoboda, Paul id: 446560C6-F248-11E8-B48F-1D18A9856A87 last_name: Swoboda - first_name: Stefanie full_name: Petra, Stefanie last_name: Petra citation: ama: 'Kuske J, Swoboda P, Petra S. A novel convex relaxation for non binary discrete tomography. In: Lauze F, Dong Y, Bjorholm Dahl A, eds. Vol 10302. Springer; 2017:235-246. doi:10.1007/978-3-319-58771-4_19' apa: 'Kuske, J., Swoboda, P., & Petra, S. (2017). A novel convex relaxation for non binary discrete tomography. In F. Lauze, Y. Dong, & A. Bjorholm Dahl (Eds.) (Vol. 10302, pp. 235–246). Presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark: Springer. https://doi.org/10.1007/978-3-319-58771-4_19' chicago: Kuske, Jan, Paul Swoboda, and Stefanie Petra. “A Novel Convex Relaxation for Non Binary Discrete Tomography.” edited by François Lauze, Yiqiu Dong, and Anders Bjorholm Dahl, 10302:235–46. Springer, 2017. https://doi.org/10.1007/978-3-319-58771-4_19. ieee: 'J. Kuske, P. Swoboda, and S. Petra, “A novel convex relaxation for non binary discrete tomography,” presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark, 2017, vol. 10302, pp. 235–246.' ista: 'Kuske J, Swoboda P, Petra S. 2017. A novel convex relaxation for non binary discrete tomography. SSVM: Scale Space and Variational Methods in Computer Vision, LNCS, vol. 10302, 235–246.' mla: Kuske, Jan, et al. A Novel Convex Relaxation for Non Binary Discrete Tomography. Edited by François Lauze et al., vol. 10302, Springer, 2017, pp. 235–46, doi:10.1007/978-3-319-58771-4_19. short: J. Kuske, P. Swoboda, S. Petra, in:, F. Lauze, Y. Dong, A. Bjorholm Dahl (Eds.), Springer, 2017, pp. 235–246. conference: end_date: 2017-06-08 location: Kolding, Denmark name: 'SSVM: Scale Space and Variational Methods in Computer Vision' start_date: 2017-06-04 date_created: 2018-12-11T11:47:41Z date_published: 2017-06-01T00:00:00Z date_updated: 2021-01-12T08:07:34Z day: '01' department: - _id: VlKo doi: 10.1007/978-3-319-58771-4_19 ec_funded: 1 editor: - first_name: François full_name: Lauze, François last_name: Lauze - first_name: Yiqiu full_name: Dong, Yiqiu last_name: Dong - first_name: Anders full_name: Bjorholm Dahl, Anders last_name: Bjorholm Dahl intvolume: ' 10302' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1703.03769 month: '06' oa: 1 oa_version: Submitted Version page: 235 - 246 project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication_identifier: isbn: - 978-331958770-7 publication_status: published publisher: Springer publist_id: '7132' quality_controlled: '1' scopus_import: 1 status: public title: A novel convex relaxation for non binary discrete tomography type: conference user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 10302 year: '2017' ... --- _id: '992' abstract: - lang: eng text: "An instance of the Constraint Satisfaction Problem (CSP) is given by a finite set of\r\nvariables, a finite domain of labels, and a set of constraints, each constraint acting on\r\na subset of the variables. The goal is to find an assignment of labels to its variables\r\nthat satisfies all constraints (or decide whether one exists). If we allow more general\r\n“soft” constraints, which come with (possibly infinite) costs of particular assignments,\r\nwe obtain instances from a richer class called Valued Constraint Satisfaction Problem\r\n(VCSP). There the goal is to find an assignment with minimum total cost.\r\nIn this thesis, we focus (assuming that P\r\n6\r\n=\r\nNP) on classifying computational com-\r\nplexity of CSPs and VCSPs under certain restricting conditions. Two results are the core\r\ncontent of the work. In one of them, we consider VCSPs parametrized by a constraint\r\nlanguage, that is the set of “soft” constraints allowed to form the instances, and finish\r\nthe complexity classification modulo (missing pieces of) complexity classification for\r\nanalogously parametrized CSP. The other result is a generalization of Edmonds’ perfect\r\nmatching algorithm. This generalization contributes to complexity classfications in two\r\nways. First, it gives a new (largest known) polynomial-time solvable class of Boolean\r\nCSPs in which every variable may appear in at most two constraints and second, it\r\nsettles full classification of Boolean CSPs with planar drawing (again parametrized by a\r\nconstraint language)." acknowledgement: FP7/2007-2013/ERC grant agreement no 616160 alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Michal full_name: Rolinek, Michal id: 3CB3BC06-F248-11E8-B48F-1D18A9856A87 last_name: Rolinek citation: ama: Rolinek M. Complexity of constraint satisfaction. 2017. doi:10.15479/AT:ISTA:th_815 apa: Rolinek, M. (2017). Complexity of constraint satisfaction. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:th_815 chicago: Rolinek, Michal. “Complexity of Constraint Satisfaction.” Institute of Science and Technology Austria, 2017. https://doi.org/10.15479/AT:ISTA:th_815. ieee: M. Rolinek, “Complexity of constraint satisfaction,” Institute of Science and Technology Austria, 2017. ista: Rolinek M. 2017. Complexity of constraint satisfaction. Institute of Science and Technology Austria. mla: Rolinek, Michal. Complexity of Constraint Satisfaction. Institute of Science and Technology Austria, 2017, doi:10.15479/AT:ISTA:th_815. short: M. Rolinek, Complexity of Constraint Satisfaction, Institute of Science and Technology Austria, 2017. date_created: 2018-12-11T11:49:35Z date_published: 2017-05-01T00:00:00Z date_updated: 2023-09-07T12:05:41Z day: '01' ddc: - '004' degree_awarded: PhD department: - _id: VlKo doi: 10.15479/AT:ISTA:th_815 ec_funded: 1 file: - access_level: open_access checksum: 81761fb939acb7585c36629f765b4373 content_type: application/pdf creator: system date_created: 2018-12-12T10:07:55Z date_updated: 2020-07-14T12:48:18Z file_id: '4654' file_name: IST-2017-815-v1+3_final_blank_signature_maybe_pdfa.pdf file_size: 786145 relation: main_file - access_level: closed checksum: 2b2d7e1d6c1c79a9795a7aa0f860baf3 content_type: application/zip creator: dernst date_created: 2019-04-05T08:43:24Z date_updated: 2020-07-14T12:48:18Z file_id: '6208' file_name: 2017_Thesis_Rolinek_source.zip file_size: 5936337 relation: source_file file_date_updated: 2020-07-14T12:48:18Z has_accepted_license: '1' language: - iso: eng month: '05' oa: 1 oa_version: Published Version page: '97' project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria publist_id: '6407' pubrep_id: '815' status: public supervisor: - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov title: Complexity of constraint satisfaction type: dissertation user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2017' ... --- _id: '1192' abstract: - lang: eng text: The main result of this paper is a generalization of the classical blossom algorithm for finding perfect matchings. Our algorithm can efficiently solve Boolean CSPs where each variable appears in exactly two constraints (we call it edge CSP) and all constraints are even Δ-matroid relations (represented by lists of tuples). As a consequence of this, we settle the complexity classification of planar Boolean CSPs started by Dvorak and Kupec. Knowing that edge CSP is tractable for even Δ-matroid constraints allows us to extend the tractability result to a larger class of Δ-matroids that includes many classes that were known to be tractable before, namely co-independent, compact, local and binary. article_processing_charge: No author: - first_name: Alexandr full_name: Kazda, Alexandr id: 3B32BAA8-F248-11E8-B48F-1D18A9856A87 last_name: Kazda - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov - first_name: Michal full_name: Rolinek, Michal id: 3CB3BC06-F248-11E8-B48F-1D18A9856A87 last_name: Rolinek citation: ama: 'Kazda A, Kolmogorov V, Rolinek M. Even delta-matroids and the complexity of planar Boolean CSPs. In: SIAM; 2017:307-326. doi:10.1137/1.9781611974782.20' apa: 'Kazda, A., Kolmogorov, V., & Rolinek, M. (2017). Even delta-matroids and the complexity of planar Boolean CSPs (pp. 307–326). Presented at the SODA: Symposium on Discrete Algorithms, Barcelona, Spain: SIAM. https://doi.org/10.1137/1.9781611974782.20' chicago: Kazda, Alexandr, Vladimir Kolmogorov, and Michal Rolinek. “Even Delta-Matroids and the Complexity of Planar Boolean CSPs,” 307–26. SIAM, 2017. https://doi.org/10.1137/1.9781611974782.20. ieee: 'A. Kazda, V. Kolmogorov, and M. Rolinek, “Even delta-matroids and the complexity of planar Boolean CSPs,” presented at the SODA: Symposium on Discrete Algorithms, Barcelona, Spain, 2017, pp. 307–326.' ista: 'Kazda A, Kolmogorov V, Rolinek M. 2017. Even delta-matroids and the complexity of planar Boolean CSPs. SODA: Symposium on Discrete Algorithms, 307–326.' mla: Kazda, Alexandr, et al. Even Delta-Matroids and the Complexity of Planar Boolean CSPs. SIAM, 2017, pp. 307–26, doi:10.1137/1.9781611974782.20. short: A. Kazda, V. Kolmogorov, M. Rolinek, in:, SIAM, 2017, pp. 307–326. conference: end_date: 2017-01019 location: Barcelona, Spain name: 'SODA: Symposium on Discrete Algorithms' start_date: 2017-01-16 date_created: 2018-12-11T11:50:38Z date_published: 2017-01-01T00:00:00Z date_updated: 2023-09-20T11:20:26Z day: '01' department: - _id: VlKo doi: 10.1137/1.9781611974782.20 ec_funded: 1 external_id: isi: - '000426965800020' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1602.03124 month: '01' oa: 1 oa_version: Submitted Version page: 307 - 326 project: - _id: 25FBA906-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '616160' name: 'Discrete Optimization in Computer Vision: Theory and Practice' publication_identifier: isbn: - 978-161197478-2 publication_status: published publisher: SIAM publist_id: '6159' quality_controlled: '1' related_material: record: - id: '6032' relation: later_version status: public status: public title: Even delta-matroids and the complexity of planar Boolean CSPs type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2017' ...