--- _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' ... --- _id: '5975' abstract: - lang: eng text: We consider the recent formulation of the algorithmic Lov ́asz Local Lemma [N. Har-vey and J. Vondr ́ak, inProceedings of FOCS, 2015, pp. 1327–1345; D. Achlioptas and F. Iliopoulos,inProceedings of SODA, 2016, pp. 2024–2038; D. Achlioptas, F. Iliopoulos, and V. Kolmogorov,ALocal Lemma for Focused Stochastic Algorithms, arXiv preprint, 2018] for finding objects that avoid“bad features,” or “flaws.” It extends the Moser–Tardos resampling algorithm [R. A. Moser andG. Tardos,J. ACM, 57 (2010), 11] to more general discrete spaces. At each step the method picks aflaw present in the current state and goes to a new state according to some prespecified probabilitydistribution (which depends on the current state and the selected flaw). However, the recent formu-lation is less flexible than the Moser–Tardos method since it requires a specific flaw selection rule,whereas the algorithm of Moser and Tardos allows an arbitrary rule (and thus can potentially beimplemented more efficiently). We formulate a new “commutativity” condition and prove that it issufficient for an arbitrary rule to work. It also enables an efficient parallelization under an additionalassumption. We then show that existing resampling oracles for perfect matchings and permutationsdo satisfy this condition. article_processing_charge: No author: - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov citation: ama: Kolmogorov V. Commutativity in the algorithmic Lovász local lemma. SIAM Journal on Computing. 2018;47(6):2029-2056. doi:10.1137/16m1093306 apa: Kolmogorov, V. (2018). Commutativity in the algorithmic Lovász local lemma. SIAM Journal on Computing. Society for Industrial & Applied Mathematics (SIAM). https://doi.org/10.1137/16m1093306 chicago: Kolmogorov, Vladimir. “Commutativity in the Algorithmic Lovász Local Lemma.” SIAM Journal on Computing. Society for Industrial & Applied Mathematics (SIAM), 2018. https://doi.org/10.1137/16m1093306. ieee: V. Kolmogorov, “Commutativity in the algorithmic Lovász local lemma,” SIAM Journal on Computing, vol. 47, no. 6. Society for Industrial & Applied Mathematics (SIAM), pp. 2029–2056, 2018. ista: Kolmogorov V. 2018. Commutativity in the algorithmic Lovász local lemma. SIAM Journal on Computing. 47(6), 2029–2056. mla: Kolmogorov, Vladimir. “Commutativity in the Algorithmic Lovász Local Lemma.” SIAM Journal on Computing, vol. 47, no. 6, Society for Industrial & Applied Mathematics (SIAM), 2018, pp. 2029–56, doi:10.1137/16m1093306. short: V. Kolmogorov, SIAM Journal on Computing 47 (2018) 2029–2056. date_created: 2019-02-13T12:59:33Z date_published: 2018-11-08T00:00:00Z date_updated: 2023-09-19T14:24:58Z day: '08' department: - _id: VlKo doi: 10.1137/16m1093306 ec_funded: 1 external_id: arxiv: - '1506.08547' isi: - '000453785100001' intvolume: ' 47' isi: 1 issue: '6' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1506.08547 month: '11' oa: 1 oa_version: Preprint page: 2029-2056 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: Society for Industrial & Applied Mathematics (SIAM) quality_controlled: '1' related_material: record: - id: '1193' relation: earlier_version status: public scopus_import: '1' status: public title: Commutativity in the algorithmic Lovász local lemma type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 47 year: '2018' ... --- _id: '5978' abstract: - lang: eng text: 'We consider the MAP-inference problem for graphical models,which is a valued constraint satisfaction problem defined onreal numbers with a natural summation operation. We proposea family of relaxations (different from the famous Sherali-Adams hierarchy), which naturally define lower bounds for itsoptimum. This family always contains a tight relaxation andwe give an algorithm able to find it and therefore, solve theinitial non-relaxed NP-hard problem.The relaxations we consider decompose the original probleminto two non-overlapping parts: an easy LP-tight part and adifficult one. For the latter part a combinatorial solver must beused. As we show in our experiments, in a number of applica-tions the second, difficult part constitutes only a small fractionof the whole problem. This property allows to significantlyreduce the computational time of the combinatorial solver andtherefore solve problems which were out of reach before.' article_processing_charge: No author: - first_name: Stefan full_name: Haller, Stefan last_name: Haller - 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: 'Haller S, Swoboda P, Savchynskyy B. Exact MAP-inference by confining combinatorial search with LP relaxation. In: Proceedings of the 32st AAAI Conference on Artificial Intelligence. AAAI Press; 2018:6581-6588.' apa: 'Haller, S., Swoboda, P., & Savchynskyy, B. (2018). Exact MAP-inference by confining combinatorial search with LP relaxation. In Proceedings of the 32st AAAI Conference on Artificial Intelligence (pp. 6581–6588). New Orleans, LU, United States: AAAI Press.' chicago: Haller, Stefan, Paul Swoboda, and Bogdan Savchynskyy. “Exact MAP-Inference by Confining Combinatorial Search with LP Relaxation.” In Proceedings of the 32st AAAI Conference on Artificial Intelligence, 6581–88. AAAI Press, 2018. ieee: S. Haller, P. Swoboda, and B. Savchynskyy, “Exact MAP-inference by confining combinatorial search with LP relaxation,” in Proceedings of the 32st AAAI Conference on Artificial Intelligence, New Orleans, LU, United States, 2018, pp. 6581–6588. ista: 'Haller S, Swoboda P, Savchynskyy B. 2018. Exact MAP-inference by confining combinatorial search with LP relaxation. Proceedings of the 32st AAAI Conference on Artificial Intelligence. AAAI: Conference on Artificial Intelligence, 6581–6588.' mla: Haller, Stefan, et al. “Exact MAP-Inference by Confining Combinatorial Search with LP Relaxation.” Proceedings of the 32st AAAI Conference on Artificial Intelligence, AAAI Press, 2018, pp. 6581–88. short: S. Haller, P. Swoboda, B. Savchynskyy, in:, Proceedings of the 32st AAAI Conference on Artificial Intelligence, AAAI Press, 2018, pp. 6581–6588. conference: end_date: 2018-02-07 location: New Orleans, LU, United States name: 'AAAI: Conference on Artificial Intelligence' start_date: 2018-02-02 date_created: 2019-02-13T13:32:48Z date_published: 2018-02-01T00:00:00Z date_updated: 2023-09-19T14:26:52Z day: '01' department: - _id: VlKo external_id: arxiv: - '2004.06370' isi: - '000485488906082' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2004.06370 month: '02' oa: 1 oa_version: Preprint page: 6581-6588 publication: Proceedings of the 32st AAAI Conference on Artificial Intelligence publication_status: published publisher: AAAI Press quality_controlled: '1' scopus_import: '1' status: public title: Exact MAP-inference by confining combinatorial search with LP relaxation type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2018' ... --- _id: '18' abstract: - lang: eng text: An N-superconcentrator is a directed, acyclic graph with N input nodes and N output nodes such that every subset of the inputs and every subset of the outputs of same cardinality can be connected by node-disjoint paths. It is known that linear-size and bounded-degree superconcentrators exist. We prove the existence of such superconcentrators with asymptotic density 25.3 (where the density is the number of edges divided by N). The previously best known densities were 28 [12] and 27.4136 [17]. article_processing_charge: No author: - 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: Kolmogorov V, Rolinek M. Superconcentrators of density 25.3. Ars Combinatoria. 2018;141(10):269-304. apa: Kolmogorov, V., & Rolinek, M. (2018). Superconcentrators of density 25.3. Ars Combinatoria. Charles Babbage Research Centre. chicago: Kolmogorov, Vladimir, and Michal Rolinek. “Superconcentrators of Density 25.3.” Ars Combinatoria. Charles Babbage Research Centre, 2018. ieee: V. Kolmogorov and M. Rolinek, “Superconcentrators of density 25.3,” Ars Combinatoria, vol. 141, no. 10. Charles Babbage Research Centre, pp. 269–304, 2018. ista: Kolmogorov V, Rolinek M. 2018. Superconcentrators of density 25.3. Ars Combinatoria. 141(10), 269–304. mla: Kolmogorov, Vladimir, and Michal Rolinek. “Superconcentrators of Density 25.3.” Ars Combinatoria, vol. 141, no. 10, Charles Babbage Research Centre, 2018, pp. 269–304. short: V. Kolmogorov, M. Rolinek, Ars Combinatoria 141 (2018) 269–304. date_created: 2018-12-11T11:44:11Z date_published: 2018-10-01T00:00:00Z date_updated: 2023-09-19T14:46:18Z day: '01' department: - _id: VlKo external_id: arxiv: - '1405.7828' isi: - '000446809500022' intvolume: ' 141' isi: 1 issue: '10' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1405.7828 month: '10' oa: 1 oa_version: Preprint page: 269 - 304 publication: Ars Combinatoria publication_identifier: issn: - 0381-7032 publication_status: published publisher: Charles Babbage Research Centre publist_id: '8037' quality_controlled: '1' scopus_import: '1' status: public title: Superconcentrators of density 25.3 type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 141 year: '2018' ... --- _id: '6032' abstract: - lang: eng text: The main result of this article 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. Using a reduction to even Δ-matroids, we then extend the tractability result to larger classes of Δ-matroids that we call efficiently coverable. It properly includes classes that were known to be tractable before, namely, co-independent, compact, local, linear, and binary, with the following caveat:We represent Δ-matroids by lists of tuples, while the last two use a representation by matrices. Since an n ×n matrix can represent exponentially many tuples, our tractability result is not strictly stronger than the known algorithm for linear and binary Δ-matroids. article_number: '22' article_processing_charge: No article_type: original 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. ACM Transactions on Algorithms. 2018;15(2). doi:10.1145/3230649 apa: Kazda, A., Kolmogorov, V., & Rolinek, M. (2018). Even delta-matroids and the complexity of planar boolean CSPs. ACM Transactions on Algorithms. ACM. https://doi.org/10.1145/3230649 chicago: Kazda, Alexandr, Vladimir Kolmogorov, and Michal Rolinek. “Even Delta-Matroids and the Complexity of Planar Boolean CSPs.” ACM Transactions on Algorithms. ACM, 2018. https://doi.org/10.1145/3230649. ieee: A. Kazda, V. Kolmogorov, and M. Rolinek, “Even delta-matroids and the complexity of planar boolean CSPs,” ACM Transactions on Algorithms, vol. 15, no. 2. ACM, 2018. ista: Kazda A, Kolmogorov V, Rolinek M. 2018. Even delta-matroids and the complexity of planar boolean CSPs. ACM Transactions on Algorithms. 15(2), 22. mla: Kazda, Alexandr, et al. “Even Delta-Matroids and the Complexity of Planar Boolean CSPs.” ACM Transactions on Algorithms, vol. 15, no. 2, 22, ACM, 2018, doi:10.1145/3230649. short: A. Kazda, V. Kolmogorov, M. Rolinek, ACM Transactions on Algorithms 15 (2018). date_created: 2019-02-17T22:59:25Z date_published: 2018-12-01T00:00:00Z date_updated: 2023-09-20T11:20:26Z day: '01' department: - _id: VlKo doi: 10.1145/3230649 ec_funded: 1 external_id: arxiv: - '1602.03124' isi: - '000468036500007' intvolume: ' 15' isi: 1 issue: '2' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1602.03124 month: '12' 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: ACM Transactions on Algorithms publication_status: published publisher: ACM quality_controlled: '1' related_material: record: - id: '1192' relation: earlier_version status: public scopus_import: '1' status: public title: Even delta-matroids and the complexity of planar boolean CSPs type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 15 year: '2018' ... --- _id: '5573' abstract: - lang: eng text: Graph matching problems for large displacement optical flow of RGB-D images. article_processing_charge: No author: - first_name: Hassan full_name: Alhaija, Hassan last_name: Alhaija - first_name: Anita full_name: Sellent, Anita last_name: Sellent - first_name: Daniel full_name: Kondermann, Daniel last_name: Kondermann - first_name: Carsten full_name: Rother, Carsten last_name: Rother citation: ama: Alhaija H, Sellent A, Kondermann D, Rother C. Graph matching problems for GraphFlow – 6D Large Displacement Scene Flow. 2018. doi:10.15479/AT:ISTA:82 apa: Alhaija, H., Sellent, A., Kondermann, D., & Rother, C. (2018). Graph matching problems for GraphFlow – 6D Large Displacement Scene Flow. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:82 chicago: Alhaija, Hassan, Anita Sellent, Daniel Kondermann, and Carsten Rother. “Graph Matching Problems for GraphFlow – 6D Large Displacement Scene Flow.” Institute of Science and Technology Austria, 2018. https://doi.org/10.15479/AT:ISTA:82. ieee: H. Alhaija, A. Sellent, D. Kondermann, and C. Rother, “Graph matching problems for GraphFlow – 6D Large Displacement Scene Flow.” Institute of Science and Technology Austria, 2018. ista: Alhaija H, Sellent A, Kondermann D, Rother C. 2018. Graph matching problems for GraphFlow – 6D Large Displacement Scene Flow, Institute of Science and Technology Austria, 10.15479/AT:ISTA:82. mla: Alhaija, Hassan, et al. Graph Matching Problems for GraphFlow – 6D Large Displacement Scene Flow. Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:82. short: H. Alhaija, A. Sellent, D. Kondermann, C. Rother, (2018). contributor: - contributor_type: researcher first_name: Paul id: 446560C6-F248-11E8-B48F-1D18A9856A87 last_name: Swoboda datarep_id: '82' date_created: 2018-12-12T12:31:36Z date_published: 2018-01-04T00:00:00Z date_updated: 2024-02-21T13:41:17Z day: '04' ddc: - '001' department: - _id: VlKo doi: 10.15479/AT:ISTA:82 file: - access_level: open_access checksum: 53c17082848e12f3c2e1b4185b578208 content_type: application/zip creator: system date_created: 2018-12-12T13:02:34Z date_updated: 2020-07-14T12:47:05Z file_id: '5600' file_name: IST-2018-82-v1+1_GraphFlowMatchingProblems.zip file_size: 1737958 relation: main_file file_date_updated: 2020-07-14T12:47:05Z has_accepted_license: '1' keyword: - graph matching - quadratic assignment problem< license: https://creativecommons.org/publicdomain/zero/1.0/ month: '01' oa: 1 oa_version: Published Version publisher: Institute of Science and Technology Austria related_material: link: - relation: research_paper url: https://doi.org/10.1007/978-3-319-24947-6_23 status: public title: Graph matching problems for GraphFlow – 6D Large Displacement Scene Flow tmp: image: /images/cc_0.png legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode name: Creative Commons Public Domain Dedication (CC0 1.0) short: CC0 (1.0) type: research_data user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2018' ... --- _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' ... --- _id: '916' abstract: - lang: eng text: We study the quadratic assignment problem, in computer vision also known as graph matching. Two leading solvers for this problem optimize the Lagrange decomposition duals with sub-gradient and dual ascent (also known as message passing) updates. We explore this direction further and propose several additional Lagrangean relaxations of the graph matching problem along with corresponding algorithms, which are all based on a common dual ascent framework. Our extensive empirical evaluation gives several theoretical insights and suggests a new state-of-the-art anytime solver for the considered problem. Our improvement over state-of-the-art is particularly visible on a new dataset with large-scale sparse problem instances containing more than 500 graph nodes each. article_processing_charge: No author: - first_name: Paul full_name: Swoboda, Paul id: 446560C6-F248-11E8-B48F-1D18A9856A87 last_name: Swoboda - first_name: Carsten full_name: Rother, Carsten last_name: Rother - first_name: Carsten full_name: Abu Alhaija, Carsten last_name: Abu Alhaija - first_name: Dagmar full_name: Kainmueller, Dagmar last_name: Kainmueller - first_name: Bogdan full_name: Savchynskyy, Bogdan last_name: Savchynskyy citation: ama: 'Swoboda P, Rother C, Abu Alhaija C, Kainmueller D, Savchynskyy B. A study of lagrangean decompositions and dual ascent solvers for graph matching. In: Vol 2017. IEEE; 2017:7062-7071. doi:10.1109/CVPR.2017.747' apa: 'Swoboda, P., Rother, C., Abu Alhaija, C., Kainmueller, D., & Savchynskyy, B. (2017). A study of lagrangean decompositions and dual ascent solvers for graph matching (Vol. 2017, pp. 7062–7071). Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States: IEEE. https://doi.org/10.1109/CVPR.2017.747' chicago: Swoboda, Paul, Carsten Rother, Carsten Abu Alhaija, Dagmar Kainmueller, and Bogdan Savchynskyy. “A Study of Lagrangean Decompositions and Dual Ascent Solvers for Graph Matching,” 2017:7062–71. IEEE, 2017. https://doi.org/10.1109/CVPR.2017.747. ieee: 'P. Swoboda, C. Rother, C. Abu Alhaija, D. Kainmueller, and B. Savchynskyy, “A study of lagrangean decompositions and dual ascent solvers for graph matching,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 7062–7071.' ista: 'Swoboda P, Rother C, Abu Alhaija C, Kainmueller D, Savchynskyy B. 2017. A study of lagrangean decompositions and dual ascent solvers for graph matching. CVPR: Computer Vision and Pattern Recognition vol. 2017, 7062–7071.' mla: Swoboda, Paul, et al. A Study of Lagrangean Decompositions and Dual Ascent Solvers for Graph Matching. Vol. 2017, IEEE, 2017, pp. 7062–71, doi:10.1109/CVPR.2017.747. short: P. Swoboda, C. Rother, C. Abu Alhaija, D. Kainmueller, B. Savchynskyy, in:, IEEE, 2017, pp. 7062–7071. conference: end_date: 2017-07-26 location: Honolulu, HA, United States name: 'CVPR: Computer Vision and Pattern Recognition' start_date: 2017-07-21 date_created: 2018-12-11T11:49:11Z date_published: 2017-01-01T00:00:00Z date_updated: 2023-09-26T15:41:40Z day: '01' ddc: - '000' department: - _id: VlKo doi: 10.1109/CVPR.2017.747 ec_funded: 1 external_id: isi: - '000418371407018' file: - access_level: open_access checksum: e38a2740daad1ea178465843b5072906 content_type: application/pdf creator: dernst date_created: 2019-01-18T12:49:38Z date_updated: 2020-07-14T12:48:15Z file_id: '5848' file_name: 2017_CVPR_Swoboda2.pdf file_size: 944332 relation: main_file file_date_updated: 2020-07-14T12:48:15Z has_accepted_license: '1' intvolume: ' 2017' isi: 1 language: - iso: eng month: '01' oa: 1 oa_version: Submitted Version page: 7062-7071 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: - 978-153860457-1 publication_status: published publisher: IEEE publist_id: '6525' quality_controlled: '1' scopus_import: '1' status: public title: A study of lagrangean decompositions and dual ascent solvers for graph matching type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 2017 year: '2017' ... --- _id: '915' abstract: - lang: eng text: We propose a dual decomposition and linear program relaxation of the NP-hard minimum cost multicut problem. Unlike other polyhedral relaxations of the multicut polytope, it is amenable to efficient optimization by message passing. Like other polyhedral relaxations, it can be tightened efficiently by cutting planes. We define an algorithm that alternates between message passing and efficient separation of cycle- and odd-wheel inequalities. This algorithm is more efficient than state-of-the-art algorithms based on linear programming, including algorithms written in the framework of leading commercial software, as we show in experiments with large instances of the problem from applications in computer vision, biomedical image analysis and data mining. article_processing_charge: No author: - first_name: Paul full_name: Swoboda, Paul id: 446560C6-F248-11E8-B48F-1D18A9856A87 last_name: Swoboda - first_name: Bjoern full_name: Andres, Bjoern last_name: Andres citation: ama: 'Swoboda P, Andres B. A message passing algorithm for the minimum cost multicut problem. In: Vol 2017. IEEE; 2017:4990-4999. doi:10.1109/CVPR.2017.530' apa: 'Swoboda, P., & Andres, B. (2017). A message passing algorithm for the minimum cost multicut problem (Vol. 2017, pp. 4990–4999). Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States: IEEE. https://doi.org/10.1109/CVPR.2017.530' chicago: Swoboda, Paul, and Bjoern Andres. “A Message Passing Algorithm for the Minimum Cost Multicut Problem,” 2017:4990–99. IEEE, 2017. https://doi.org/10.1109/CVPR.2017.530. ieee: 'P. Swoboda and B. Andres, “A message passing algorithm for the minimum cost multicut problem,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 4990–4999.' ista: 'Swoboda P, Andres B. 2017. A message passing algorithm for the minimum cost multicut problem. CVPR: Computer Vision and Pattern Recognition vol. 2017, 4990–4999.' mla: Swoboda, Paul, and Bjoern Andres. A Message Passing Algorithm for the Minimum Cost Multicut Problem. Vol. 2017, IEEE, 2017, pp. 4990–99, doi:10.1109/CVPR.2017.530. short: P. Swoboda, B. Andres, in:, IEEE, 2017, pp. 4990–4999. conference: end_date: 2017-07-26 location: Honolulu, HA, United States name: 'CVPR: Computer Vision and Pattern Recognition' start_date: 2017-07-21 date_created: 2018-12-11T11:49:11Z date_published: 2017-07-01T00:00:00Z date_updated: 2023-09-26T15:43:27Z day: '01' ddc: - '000' department: - _id: VlKo doi: 10.1109/CVPR.2017.530 ec_funded: 1 external_id: isi: - '000418371405009' file: - access_level: open_access checksum: 7e51dacefa693574581a32da3eff63dc content_type: application/pdf creator: dernst date_created: 2019-01-18T12:52:46Z date_updated: 2020-07-14T12:48:15Z file_id: '5849' file_name: Swoboda_A_Message_Passing_CVPR_2017_paper.pdf file_size: 883264 relation: main_file file_date_updated: 2020-07-14T12:48:15Z has_accepted_license: '1' intvolume: ' 2017' isi: 1 language: - iso: eng month: '07' oa: 1 oa_version: Submitted Version page: 4990-4999 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: - 978-153860457-1 publication_status: published publisher: IEEE publist_id: '6526' quality_controlled: '1' scopus_import: '1' status: public title: A message passing algorithm for the minimum cost multicut problem type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 2017 year: '2017' ... --- _id: '917' abstract: - lang: eng text: We propose a general dual ascent framework for Lagrangean decomposition of combinatorial problems. Although methods of this type have shown their efficiency for a number of problems, so far there was no general algorithm applicable to multiple problem types. In this work, we propose such a general algorithm. It depends on several parameters, which can be used to optimize its performance in each particular setting. We demonstrate efficacy of our method on graph matching and multicut problems, where it outperforms state-of-the-art solvers including those based on subgradient optimization and off-the-shelf linear programming solvers. article_processing_charge: No author: - first_name: Paul full_name: Swoboda, Paul id: 446560C6-F248-11E8-B48F-1D18A9856A87 last_name: Swoboda - first_name: Jan full_name: Kuske, Jan last_name: Kuske - first_name: Bogdan full_name: Savchynskyy, Bogdan last_name: Savchynskyy citation: ama: 'Swoboda P, Kuske J, Savchynskyy B. A dual ascent framework for Lagrangean decomposition of combinatorial problems. In: Vol 2017. IEEE; 2017:4950-4960. doi:10.1109/CVPR.2017.526' apa: 'Swoboda, P., Kuske, J., & Savchynskyy, B. (2017). A dual ascent framework for Lagrangean decomposition of combinatorial problems (Vol. 2017, pp. 4950–4960). Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States: IEEE. https://doi.org/10.1109/CVPR.2017.526' chicago: Swoboda, Paul, Jan Kuske, and Bogdan Savchynskyy. “A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems,” 2017:4950–60. IEEE, 2017. https://doi.org/10.1109/CVPR.2017.526. ieee: 'P. Swoboda, J. Kuske, and B. Savchynskyy, “A dual ascent framework for Lagrangean decomposition of combinatorial problems,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 4950–4960.' ista: 'Swoboda P, Kuske J, Savchynskyy B. 2017. A dual ascent framework for Lagrangean decomposition of combinatorial problems. CVPR: Computer Vision and Pattern Recognition vol. 2017, 4950–4960.' mla: Swoboda, Paul, et al. A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems. Vol. 2017, IEEE, 2017, pp. 4950–60, doi:10.1109/CVPR.2017.526. short: P. Swoboda, J. Kuske, B. Savchynskyy, in:, IEEE, 2017, pp. 4950–4960. conference: end_date: 2017-07-26 location: Honolulu, HA, United States name: 'CVPR: Computer Vision and Pattern Recognition' start_date: 2017-07-21 date_created: 2018-12-11T11:49:11Z date_published: 2017-07-01T00:00:00Z date_updated: 2023-09-26T15:41:11Z day: '01' ddc: - '000' department: - _id: VlKo doi: 10.1109/CVPR.2017.526 ec_funded: 1 external_id: isi: - '000418371405005' file: - access_level: open_access checksum: 72fd291046bd8e5717961bd68f6b6f03 content_type: application/pdf creator: dernst date_created: 2019-01-18T12:45:55Z date_updated: 2020-07-14T12:48:15Z file_id: '5847' file_name: 2017_CVPR_Swoboda.pdf file_size: 898652 relation: main_file file_date_updated: 2020-07-14T12:48:15Z has_accepted_license: '1' intvolume: ' 2017' isi: 1 language: - iso: eng month: '07' oa: 1 oa_version: Submitted Version page: 4950-4960 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: - 978-153860457-1 publication_status: published publisher: IEEE publist_id: '6524' quality_controlled: '1' scopus_import: '1' status: public title: A dual ascent framework for Lagrangean decomposition of combinatorial problems type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 2017 year: '2017' ... --- _id: '274' abstract: - lang: eng text: We consider the problem of estimating the partition function Z(β)=∑xexp(−β(H(x)) of a Gibbs distribution with a Hamilton H(⋅), or more precisely the logarithm of the ratio q=lnZ(0)/Z(β). It has been recently shown how to approximate q with high probability assuming the existence of an oracle that produces samples from the Gibbs distribution for a given parameter value in [0,β]. The current best known approach due to Huber [9] uses O(qlnn⋅[lnq+lnlnn+ε−2]) oracle calls on average where ε is the desired accuracy of approximation and H(⋅) is assumed to lie in {0}∪[1,n]. We improve the complexity to O(qlnn⋅ε−2) oracle calls. We also show that the same complexity can be achieved if exact oracles are replaced with approximate sampling oracles that are within O(ε2qlnn) variation distance from exact oracles. Finally, we prove a lower bound of Ω(q⋅ε−2) oracle calls under a natural model of computation. article_processing_charge: No author: - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov citation: ama: 'Kolmogorov V. A faster approximation algorithm for the Gibbs partition function. In: Proceedings of the 31st Conference On Learning Theory. Vol 75. ML Research Press; 2017:228-249.' apa: Kolmogorov, V. (2017). A faster approximation algorithm for the Gibbs partition function. In Proceedings of the 31st Conference On Learning Theory (Vol. 75, pp. 228–249). ML Research Press. chicago: Kolmogorov, Vladimir. “A Faster Approximation Algorithm for the Gibbs Partition Function.” In Proceedings of the 31st Conference On Learning Theory, 75:228–49. ML Research Press, 2017. ieee: V. Kolmogorov, “A faster approximation algorithm for the Gibbs partition function,” in Proceedings of the 31st Conference On Learning Theory, 2017, vol. 75, pp. 228–249. ista: 'Kolmogorov V. 2017. A faster approximation algorithm for the Gibbs partition function. Proceedings of the 31st Conference On Learning Theory. COLT: Annual Conference on Learning Theory vol. 75, 228–249.' mla: Kolmogorov, Vladimir. “A Faster Approximation Algorithm for the Gibbs Partition Function.” Proceedings of the 31st Conference On Learning Theory, vol. 75, ML Research Press, 2017, pp. 228–49. short: V. Kolmogorov, in:, Proceedings of the 31st Conference On Learning Theory, ML Research Press, 2017, pp. 228–249. conference: end_date: 2018-07-09 name: 'COLT: Annual Conference on Learning Theory ' start_date: 2018-07-06 date_created: 2018-12-11T11:45:33Z date_published: 2017-12-27T00:00:00Z date_updated: 2023-10-17T12:32:13Z day: '27' ddc: - '510' department: - _id: VlKo ec_funded: 1 external_id: arxiv: - '1608.04223' file: - access_level: open_access checksum: 89db06a0e8083524449cb59b56bf4e5b content_type: application/pdf creator: dernst date_created: 2020-05-12T09:23:27Z date_updated: 2020-07-14T12:45:45Z file_id: '7820' file_name: 2018_PMLR_Kolmogorov.pdf file_size: 408974 relation: main_file file_date_updated: 2020-07-14T12:45:45Z has_accepted_license: '1' intvolume: ' 75' language: - iso: eng license: https://creativecommons.org/licenses/by/4.0/ month: '12' oa: 1 oa_version: Published Version page: 228-249 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 31st Conference On Learning Theory publication_status: published publisher: ML Research Press publist_id: '7628' quality_controlled: '1' status: public title: A faster approximation algorithm for the Gibbs partition function 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: 75 year: '2017' ... --- _id: '5561' abstract: - lang: eng text: 'Graph matching problems as described in "Active Graph Matching for Automatic Joint Segmentation and Annotation of C. Elegans." by Kainmueller, Dagmar and Jug, Florian and Rother, Carsten and Myers, Gene, MICCAI 2014. Problems are in OpenGM2 hdf5 format (see http://hciweb2.iwr.uni-heidelberg.de/opengm/) and a custom text format used by the feature matching solver described in "Feature Correspondence via Graph Matching: Models and Global Optimization." by Lorenzo Torresani, Vladimir Kolmogorov and Carsten Rother, ECCV 2008, code at http://pub.ist.ac.at/~vnk/software/GraphMatching-v1.02.src.zip. ' acknowledgement: We thank Vladimir Kolmogorov and Stephan Saalfeld forinspiring discussions. article_processing_charge: No author: - first_name: Dagmar full_name: Kainmueller, Dagmar last_name: Kainmueller - first_name: Florian full_name: Jug, Florian last_name: Jug - first_name: Carsten full_name: Rother, Carsten last_name: Rother - first_name: Gene full_name: Meyers, Gene last_name: Meyers citation: ama: Kainmueller D, Jug F, Rother C, Meyers G. Graph matching problems for annotating C. Elegans. 2017. doi:10.15479/AT:ISTA:57 apa: Kainmueller, D., Jug, F., Rother, C., & Meyers, G. (2017). Graph matching problems for annotating C. Elegans. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:57 chicago: Kainmueller, Dagmar, Florian Jug, Carsten Rother, and Gene Meyers. “Graph Matching Problems for Annotating C. Elegans.” Institute of Science and Technology Austria, 2017. https://doi.org/10.15479/AT:ISTA:57. ieee: D. Kainmueller, F. Jug, C. Rother, and G. Meyers, “Graph matching problems for annotating C. Elegans.” Institute of Science and Technology Austria, 2017. ista: Kainmueller D, Jug F, Rother C, Meyers G. 2017. Graph matching problems for annotating C. Elegans, Institute of Science and Technology Austria, 10.15479/AT:ISTA:57. mla: Kainmueller, Dagmar, et al. Graph Matching Problems for Annotating C. Elegans. Institute of Science and Technology Austria, 2017, doi:10.15479/AT:ISTA:57. short: D. Kainmueller, F. Jug, C. Rother, G. Meyers, (2017). datarep_id: '57' date_created: 2018-12-12T12:31:32Z date_published: 2017-02-13T00:00:00Z date_updated: 2024-02-21T13:46:31Z day: '13' ddc: - '000' department: - _id: VlKo doi: 10.15479/AT:ISTA:57 file: - access_level: open_access checksum: 3dc3e1306a66028a34181ebef2923139 content_type: application/zip creator: system date_created: 2018-12-12T13:02:54Z date_updated: 2020-07-14T12:47:03Z file_id: '5614' file_name: IST-2017-57-v1+1_wormMatchingProblems.zip file_size: 327042819 relation: main_file file_date_updated: 2020-07-14T12:47:03Z has_accepted_license: '1' keyword: - graph matching - feature matching - QAP - MAP-inference month: '02' oa: 1 oa_version: Published Version publisher: Institute of Science and Technology Austria status: public title: Graph matching problems for annotating C. Elegans tmp: image: /images/cc_0.png legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode name: Creative Commons Public Domain Dedication (CC0 1.0) short: CC0 (1.0) type: research_data user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2017' ...