Shehu Y, Gibali A, Sagratella S. 2020. Inertial projection-type methods for solving quasi-variational inequalities in real Hilbert spaces. Journal of Optimization Theory and Applications. 184, 877–894.
LibreCat: https://research-explorer.ista.ac.at/record/7161
DOI: 10.1007/s10957-019-01616-6
WoS: 000511805200009

Kolmogorov V. 2019. Testing the complexity of a valued CSP language. 46th International Colloquium on Automata, Languages and Programming. ICALP 2019: International Colloquim on Automata, Languages and Programming, LIPIcs, vol. 132, 77:1-77:12.
LibreCat: https://research-explorer.ista.ac.at/record/6725
DOI: 10.4230/LIPICS.ICALP.2019.77
arXiv: 1803.02289

Shehu Y. 2019. Convergence results of forward-backward algorithms for sum of monotone operators in Banach spaces. Results in Mathematics. 74(4), 138.
LibreCat: https://research-explorer.ista.ac.at/record/6596
DOI: 10.1007/s00025-019-1061-4
WoS: 000473237500002
arXiv: 2101.09068

Shehu Y, Iyiola OS, Li X-H, Dong Q-L. 2019. Convergence analysis of projection method for variational inequalities. Computational and Applied Mathematics. 38(4), 161.
LibreCat: https://research-explorer.ista.ac.at/record/7000
DOI: 10.1007/s40314-019-0955-9
WoS: 000488973100005
arXiv: 2101.09081

Achlioptas D, Iliopoulos F, Kolmogorov V. 2019. A local lemma for focused stochastical algorithms. SIAM Journal on Computing. 48(5), 1583–1602.
LibreCat: https://research-explorer.ista.ac.at/record/7412
DOI: 10.1137/16m109332x
WoS: 000493900200005
arXiv: 1809.01537

Swoboda P, Kolmogorov V. 2019. Map inference via block-coordinate Frank-Wolfe algorithm. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition vol. 2019–June, 11138–11147.
LibreCat: https://research-explorer.ista.ac.at/record/7468
DOI: 10.1109/CVPR.2019.01140
WoS: 000542649304076
arXiv: 1806.05049

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.
LibreCat: https://research-explorer.ista.ac.at/record/7639
DOI: 10.1109/ICCVW.2019.00097
WoS: 000554591600090

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.
LibreCat: https://research-explorer.ista.ac.at/record/703
DOI: 10.1109/TPAMI.2017.2730884
arXiv: 1508.07902

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.
LibreCat: https://research-explorer.ista.ac.at/record/10864
DOI: 10.1007/978-3-319-74772-9_7
arXiv: 1502.01072

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.
LibreCat: https://research-explorer.ista.ac.at/record/273
DOI: 10.1109/cvpr.2018.00389
WoS: 000457843603087
arXiv: 1604.08269

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.
LibreCat: https://research-explorer.ista.ac.at/record/193
DOI: 10.1145/3196494.3196534
WoS: 000516620100005

Kolmogorov V. 2018. Commutativity in the algorithmic Lovász local lemma. SIAM Journal on Computing. 47(6), 2029–2056.
LibreCat: https://research-explorer.ista.ac.at/record/5975
DOI: 10.1137/16m1093306
WoS: 000453785100001
arXiv: 1506.08547

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.
LibreCat: https://research-explorer.ista.ac.at/record/5978
WoS: 000485488906082
arXiv: 2004.06370

Kolmogorov V, Rolinek M. 2018. Superconcentrators of density 25.3. Ars Combinatoria. 141(10), 269–304.
LibreCat: https://research-explorer.ista.ac.at/record/18
WoS: 000446809500022
arXiv: 1405.7828

Kazda A, Kolmogorov V, Rolinek M. 2018. Even delta-matroids and the complexity of planar boolean CSPs. ACM Transactions on Algorithms. 15(2), 22.
LibreCat: https://research-explorer.ista.ac.at/record/6032
DOI: 10.1145/3230649
WoS: 000468036500007
arXiv: 1602.03124

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.
LibreCat: https://research-explorer.ista.ac.at/record/641
DOI: 10.1007/978-3-319-58771-4_26

Kolmogorov V, Krokhin A, Rolinek M. 2017. The complexity of general-valued CSPs. SIAM Journal on Computing. 46(3), 1087–1110.
LibreCat: https://research-explorer.ista.ac.at/record/644
DOI: 10.1137/16M1091836

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.
LibreCat: https://research-explorer.ista.ac.at/record/646
DOI: 10.1007/978-3-319-58771-4_19

Rolinek M. 2017. Complexity of constraint satisfaction. Institute of Science and Technology Austria.
LibreCat: https://research-explorer.ista.ac.at/record/992
DOI: 10.15479/AT:ISTA:th_815

Kazda A, Kolmogorov V, Rolinek M. 2017. Even delta-matroids and the complexity of planar Boolean CSPs. SODA: Symposium on Discrete Algorithms, 307–326.
LibreCat: https://research-explorer.ista.ac.at/record/1192
DOI: 10.1137/1.9781611974782.20
WoS: 000426965800020

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.
LibreCat: https://research-explorer.ista.ac.at/record/916
DOI: 10.1109/CVPR.2017.747
WoS: 000418371407018

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.
LibreCat: https://research-explorer.ista.ac.at/record/915
DOI: 10.1109/CVPR.2017.530
WoS: 000418371405009

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.
LibreCat: https://research-explorer.ista.ac.at/record/917
DOI: 10.1109/CVPR.2017.526
WoS: 000418371405005

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.
LibreCat: https://research-explorer.ista.ac.at/record/274
arXiv: 1608.04223

Alwen JF, Chen B, Kamath Hosdurg C, Kolmogorov V, Pietrzak KZ, Tessaro S. 2016. On the complexity of scrypt and proofs of space in the parallel random oracle model. EUROCRYPT: Theory and Applications of Cryptographic Techniques, LNCS, vol. 9666, 358–387.
LibreCat: https://research-explorer.ista.ac.at/record/1231
DOI: 10.1007/978-3-662-49896-5_13

Barto L, Kazda A. 2016. Deciding absorption. International Journal of Algebra and Computation. 26(5), 1033–1060.
LibreCat: https://research-explorer.ista.ac.at/record/1353
DOI: 10.1142/S0218196716500430

Kolmogorov V, Pock T, Rolinek M. 2016. Total variation on a tree. SIAM Journal on Imaging Sciences. 9(2), 605–636.
LibreCat: https://research-explorer.ista.ac.at/record/1377
DOI: 10.1137/15M1010257

Kazda A. 2016. CSP for binary conservative relational structures. Algebra Universalis. 75(1), 75–84.
LibreCat: https://research-explorer.ista.ac.at/record/1612
DOI: 10.1007/s00012-015-0358-8

Kolmogorov V. 2016. Commutativity in the algorithmic Lovasz local lemma. Proceedings - Annual IEEE Symposium on Foundations of Computer Science. FOCS: Foundations of Computer Science vol. 2016–December, 7782993.
LibreCat: https://research-explorer.ista.ac.at/record/1193
DOI: 10.1109/FOCS.2016.88
arXiv: 1506.08547

Kolmogorov V, Takhanov R. 2016. Inference algorithms for pattern-based CRFs on sequence data. Algorithmica. 76(1), 17–46.
LibreCat: https://research-explorer.ista.ac.at/record/1794
DOI: 10.1007/s00453-015-0017-7
arXiv: 1210.0508

Kolmogorov V, Rolinek M, Takhanov R. 2015. Effectiveness of structural restrictions for hybrid CSPs. 26th International Symposium. ISAAC: International Symposium on Algorithms and Computation, LNCS, vol. 9472, 566–577.
LibreCat: https://research-explorer.ista.ac.at/record/1636
DOI: 10.1007/978-3-662-48971-0_48
arXiv: 1504.07067

Kolmogorov V. 2015. A new look at reweighted message passing. IEEE Transactions on Pattern Analysis and Machine Intelligence. 37(5), 919–930.
LibreCat: https://research-explorer.ista.ac.at/record/1841
DOI: 10.1109/TPAMI.2014.2363465

Shah N, Kolmogorov V, Lampert C. 2015. A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle. CVPR: Computer Vision and Pattern Recognition, 2737–2745.
LibreCat: https://research-explorer.ista.ac.at/record/1859
DOI: 10.1109/CVPR.2015.7298890

Kolmogorov V, Thapper J, divný S. 2015. The power of linear programming for general-valued CSPs. SIAM Journal on Computing. 44(1), 1–36.
LibreCat: https://research-explorer.ista.ac.at/record/2271
DOI: 10.1137/130945648
arXiv: 1311.4219

Kolmogorov V, Krokhin A, Rolinek M. 2015. The complexity of general-valued CSPs. FOCS: Foundations of Computer Science, 56th Annual Symposium on Foundations of Computer Science, , 1246–1258.
LibreCat: https://research-explorer.ista.ac.at/record/1637
DOI: 10.1109/FOCS.2015.80

Dziembowski S, Faust S, Kolmogorov V, Pietrzak KZ. 2015. Proofs of space. 35th Annual Cryptology Conference. CRYPTO: International Cryptology Conference, LNCS, vol. 9216, 585–605.
LibreCat: https://research-explorer.ista.ac.at/record/1675
DOI: 10.1007/978-3-662-48000-7_29

Olsson C, Ulen J, Boykov Y, Kolmogorov V. 2014. Partial enumeration and curvature regularization. ICCV: International Conference on Computer Vision, 2936–2943.
LibreCat: https://research-explorer.ista.ac.at/record/2275
DOI: 10.1109/ICCV.2013.365

Huszár K, Rolinek M. Playful Math - An introduction to mathematical games, IST Austria, 5p.
LibreCat: https://research-explorer.ista.ac.at/record/7038

Bachrach Y, Kohli P, Kolmogorov V, Zadimoghaddam M. 2013. Optimal Coalition Structures in Cooperative Graph Games. AAAI: Conference on Artificial Intelligence, 81–87.
LibreCat: https://research-explorer.ista.ac.at/record/2270
arXiv: 1108.5248

Kolmogorov V. 2013. Reweighted message passing revisited, IST Austria,p.
LibreCat: https://research-explorer.ista.ac.at/record/2273

Gridchyn I, Kolmogorov V. 2013. Potts model, parametric maxflow and k-submodular functions. ICCV: International Conference on Computer Vision, 2320–2327.
LibreCat: https://research-explorer.ista.ac.at/record/2276
DOI: 10.1109/ICCV.2013.288
arXiv: 1310.1771

Kolmogorov V. 2013. The power of linear programming for finite-valued CSPs: A constructive characterization. ICALP: Automata, Languages and Programming, LNCS, vol. 7965, 625–636.
LibreCat: https://research-explorer.ista.ac.at/record/2518
DOI: 10.1007/978-3-642-39206-1_53
arXiv: 1207.7213

Kolmogorov V, divný S. 2013. The complexity of conservative valued CSPs. Journal of the ACM. 60(2), 10.
LibreCat: https://research-explorer.ista.ac.at/record/2828
DOI: 10.1145/2450142.2450146
arXiv: 1110.2809

Chen C, Kolmogorov V, Yan Z, Metaxas D, Lampert C. 2013. Computing the M most probable modes of a graphical model.  AISTATS: Conference on Uncertainty in Artificial Intelligence,  JMLR: W&;CP, vol. 31, 161–169.
LibreCat: https://research-explorer.ista.ac.at/record/2901

Takhanov R, Kolmogorov V. 2013. Inference algorithms for pattern-based CRFs on sequence data. ICML’13 Proceedings of the 30th International Conference on International. ICML: International Conference on Machine Learning, JMLR, vol. 28, 145–153.
LibreCat: https://research-explorer.ista.ac.at/record/2272

Dziembowski S, Faust S, Kolmogorov V, Pietrzak KZ. 2013. Proofs of Space, IST Austria,p.
LibreCat: https://research-explorer.ista.ac.at/record/2274

Huber A, Kolmogorov V. 2012. Towards minimizing k-submodular functions. ISCO: International Symposium on Combinatorial Optimization, LNCS, vol. 7422, 451–462.
LibreCat: https://research-explorer.ista.ac.at/record/2930
DOI: 10.1007/978-3-642-32147-4_40

Kolmogorov V, Schoenemann T. 2012. Generalized sequential tree-reweighted message passing. arXiv, .
LibreCat: https://research-explorer.ista.ac.at/record/2928
arXiv: 1205.6352

Torresani L, Kolmogorov V, Rother C. 2012. A dual decomposition approach to feature correspondence. IEEE Transactions on Pattern Analysis and Machine Intelligence. 35(2), 259–271.
LibreCat: https://research-explorer.ista.ac.at/record/2931
DOI: 10.1109/TPAMI.2012.105

Kolmogorov V. 2012. Minimizing a sum of submodular functions. Discrete Applied Mathematics. 160(15), 2246–2258.
LibreCat: https://research-explorer.ista.ac.at/record/3117
DOI: 10.1016/j.dam.2012.05.025

Kolmogorov V. 2012. Generalized roof duality and bisubmodular functions. Discrete Applied Mathematics. 160(4–5), 416–426.
LibreCat: https://research-explorer.ista.ac.at/record/3257
DOI: 10.1016/j.dam.2011.10.026
arXiv: 1005.2305

Korc F, Kolmogorov V, Lampert C. 2012. Approximating marginals using discrete energy minimization. ICML: International Conference on Machine Learning, Inferning 2012, .
LibreCat: https://research-explorer.ista.ac.at/record/3124

Korc F, Kolmogorov V, Lampert C. 2012. Approximating marginals using discrete energy minimization, IST Austria, 13p.
LibreCat: https://research-explorer.ista.ac.at/record/5396
DOI: 10.15479/AT:IST-2012-0003