Dvorak, M., &; Kolmogorov, V. (2024). Generalized minimum 0-extension problem and discrete convexity. Mathematical Programming. Springer Nature.https://doi.org/10.1007/s10107-024-02064-5 
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arXiv: 2109.10203

Harris, D. G., &; Kolmogorov, V. (2023). Parameter estimation for Gibbs distributions. In 50th International Colloquium on Automata, Languages, and Programming (Vol. 261). Paderborn, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.ICALP.2023.72 
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Dvorak, M., &; Blanchette, J. (2023). Closure properties of general grammars - formally verified. In 14th International Conference on Interactive Theorem Proving (Vol. 268). Bialystok, Poland: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.ITP.2023.15 
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arXiv: 2302.06420

Kolmogorov, V. (2023). Solving relaxations of MAP-MRF problems: Combinatorial in-face Frank-Wolfe directions. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Vol. 2023, pp. 11980–11989). Vancouver, Canada: IEEE.https://doi.org/10.1109/CVPR52729.2023.01153 
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arXiv: 2010.09567

Takhanov, R., &; Kolmogorov, V. (2022). Combining pattern-based CRFs and weighted context-free grammars. Intelligent Data Analysis. IOS Press.https://doi.org/10.3233/IDA-205623 
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Iyiola, O. S., Enyi, C. D., &; Shehu, Y. (2022). Reflected three-operator splitting method for monotone inclusion problem. Optimization Methods and Software. Taylor and Francis.https://doi.org/10.1080/10556788.2021.1924715 
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Ogbuisi, F. U., Shehu, Y., &; Yao, J. C. (2022). Convergence analysis of new inertial method for the split common null point problem. Optimization. Taylor and Francis.https://doi.org/10.1080/02331934.2021.1914035 
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Shehu, Y., &; Iyiola, O. S. (2022). Weak convergence for variational inequalities with inertial-type method. Applicable Analysis. Taylor &; Francis.https://doi.org/10.1080/00036811.2020.1736287 
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arXiv: 2101.08057

Harris, D. G., Iliopoulos, F., &; Kolmogorov, V. (2021). A new notion of commutativity for the algorithmic Lovász Local Lemma. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (Vol. 207). Virtual: Schloss Dagstuhl - Leibniz Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2021.31 
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arXiv: 2008.05569

Kolmogorov, V., &; Pock, T. (2021). One-sided Frank-Wolfe algorithms for saddle problems. In 38th International Conference on Machine Learning. Virtual.
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arXiv: 2101.12617

Dvorak, M., &; Nicholson, S. (n.d.). Massively winning configurations in the convex grabbing game on the plane. In Proceedings of the 33rd Canadian Conference on Computational Geometry. Halifax, NS, Canada.
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arXiv: 2106.11247

Izuchukwu, C., &; Shehu, Y. (2021). New inertial projection methods for solving multivalued variational inequality problems beyond monotonicity. Networks and Spatial Economics. Springer Nature.https://doi.org/10.1007/s11067-021-09517-w 
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Bloch-Hansen, A., Samei, N., &; Solis-Oba, R. (2021). Experimental evaluation of a local search approximation algorithm for the multiway cut problem. In Conference on Algorithms and Discrete Applied Mathematics (Vol. 12601, pp. 346–358). Rupnagar, India: Springer Nature.https://doi.org/10.1007/978-3-030-67899-9_28 
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Shehu, Y., Iyiola, O. S., Thong, D. V., &; Van, N. T. C. (2021). An inertial subgradient extragradient algorithm extended to pseudomonotone equilibrium problems. Mathematical Methods of Operations Research. Springer Nature.https://doi.org/10.1007/s00186-020-00730-w 
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Iyiola, O. S., &; Shehu, Y. (2021). New convergence results for inertial Krasnoselskii–Mann iterations in Hilbert spaces with applications. Results in Mathematics. Springer Nature.https://doi.org/10.1007/s00025-021-01381-x 
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Shehu, Y., Dong, Q.-L., Liu, L.-L., &; Yao, J.-C. (2021). New strong convergence method for the sum of two maximal monotone operators. Optimization and Engineering. Springer Nature.https://doi.org/10.1007/s11081-020-09544-5 
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Shehu, Y., &; Gibali, A. (2021). New inertial relaxed method for solving split feasibilities. Optimization Letters. Springer Nature.https://doi.org/10.1007/s11590-020-01603-1 
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Shehu, Y., Li, X.-H., &; Dong, Q.-L. (2020). An efficient projection-type method for monotone variational inequalities in Hilbert spaces. Numerical Algorithms. Springer Nature.https://doi.org/10.1007/s11075-019-00758-y 
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Shehu, Y., &; Iyiola, O. S. (2020). Projection methods with alternating inertial steps for variational inequalities: Weak and linear convergence. Applied Numerical Mathematics. Elsevier.https://doi.org/10.1016/j.apnum.2020.06.009 
LibreCat: https://research-explorer.ista.ac.at/record/8077
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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. Springer Nature.https://doi.org/10.1007/s10957-019-01616-6 
LibreCat: https://research-explorer.ista.ac.at/record/7161
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WoS: 000511805200009

Kolmogorov, V. (2019). Testing the complexity of a valued CSP language. In 46th International Colloquium on Automata, Languages and Programming (Vol. 132, p. 77:1-77:12). Patras, Greece: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPICS.ICALP.2019.77 
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arXiv: 1803.02289

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

Shehu, Y., Iyiola, O. S., Li, X.-H., &; Dong, Q.-L. (2019). Convergence analysis of projection method for variational inequalities. Computational and Applied Mathematics. Springer Nature.https://doi.org/10.1007/s40314-019-0955-9 
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. SIAM.https://doi.org/10.1137/16m109332x 
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. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Vol. 2019–June). Long Beach, CA, United States: IEEE.https://doi.org/10.1109/CVPR.2019.01140 
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, 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 
LibreCat: https://research-explorer.ista.ac.at/record/7639
DOI: 10.1109/ICCVW.2019.00097
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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 
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 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 
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, 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 
LibreCat: https://research-explorer.ista.ac.at/record/273
DOI: 10.1109/cvpr.2018.00389
WoS: 000457843603087
arXiv: 1604.08269

Alwen, J. F., Gazi, P., Kamath Hosdurg, C., Klein, K., Osang, G. F., Pietrzak, K. Z.,https://doi.org/10.1145/3196494.3196534__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 
LibreCat: https://research-explorer.ista.ac.at/record/193
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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 
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arXiv: 1506.08547

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

Kolmogorov, V., &; Rolinek, M. (2018). Superconcentrators of density 25.3. Ars Combinatoria. Charles Babbage Research Centre.
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. ACM.https://doi.org/10.1145/3230649 
LibreCat: https://research-explorer.ista.ac.at/record/6032
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arXiv: 1602.03124

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 
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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. SIAM.https://doi.org/10.1137/16M1091836 
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. 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 
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Rolinek, M. (2017). Complexity of constraint satisfaction. Institute of Science and Technology Austria.https://doi.org/10.15479/AT:ISTA:th_815 
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DOI: 10.15479/AT:ISTA:th_815

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 
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DOI: 10.1137/1.9781611974782.20
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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 
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DOI: 10.1109/CVPR.2017.747
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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 
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DOI: 10.1109/CVPR.2017.530
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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 
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. In Proceedings of the 31st Conference On Learning Theory (Vol. 75, pp. 228–249). ML Research Press.
LibreCat: https://research-explorer.ista.ac.at/record/274
arXiv: 1608.04223

Alwen, J. F., Chen, B., Kamath Hosdurg, C., Kolmogorov, V., Pietrzak, K. Z., &; Tessaro, S. (2016). On the complexity of scrypt and proofs of space in the parallel random oracle model (Vol. 9666, pp. 358–387). Presented at the EUROCRYPT: Theory and Applications of Cryptographic Techniques, Vienna, Austria: Springer.https://doi.org/10.1007/978-3-662-49896-5_13 
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. World Scientific Publishing.https://doi.org/10.1142/S0218196716500430 
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. Society for Industrial and Applied Mathematics .https://doi.org/10.1137/15M1010257 
LibreCat: https://research-explorer.ista.ac.at/record/1377
DOI: 10.1137/15M1010257

Kazda, A. (2016). CSP for binary conservative relational structures. Algebra Universalis. Springer.https://doi.org/10.1007/s00012-015-0358-8 
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. In Proceedings - Annual IEEE Symposium on Foundations of Computer Science (Vol. 2016–December). New Brunswick, NJ, USA : IEEE.https://doi.org/10.1109/FOCS.2016.88 
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. Springer.https://doi.org/10.1007/s00453-015-0017-7 
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. In 26th International Symposium (Vol. 9472, pp. 566–577). Nagoya, Japan: Springer Nature.https://doi.org/10.1007/978-3-662-48971-0_48 
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. IEEE.https://doi.org/10.1109/TPAMI.2014.2363465 
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 (pp. 2737–2745). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, USA: IEEE.https://doi.org/10.1109/CVPR.2015.7298890 
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. SIAM.https://doi.org/10.1137/130945648 
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 (pp. 1246–1258). Presented at the FOCS: Foundations of Computer Science, Berkeley, CA, United States: IEEE.https://doi.org/10.1109/FOCS.2015.80 
LibreCat: https://research-explorer.ista.ac.at/record/1637
DOI: 10.1109/FOCS.2015.80

Dziembowski, S., Faust, S., Kolmogorov, V., &; Pietrzak, K. Z. (2015). Proofs of space. In 35th Annual Cryptology Conference (Vol. 9216, pp. 585–605). Santa Barbara, CA, United States: Springer.https://doi.org/10.1007/978-3-662-48000-7_29 
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 (pp. 2936–2943). Presented at the ICCV: International Conference on Computer Vision, Sydney, Australia: IEEE.https://doi.org/10.1109/ICCV.2013.365 
LibreCat: https://research-explorer.ista.ac.at/record/2275
DOI: 10.1109/ICCV.2013.365

Huszár, K., &; Rolinek, M. (n.d.). Playful Math - An introduction to mathematical games. IST Austria.
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 (pp. 81–87). Presented at the AAAI: Conference on Artificial Intelligence, Bellevue, WA, United States: AAAI Press.
LibreCat: https://research-explorer.ista.ac.at/record/2270
arXiv: 1108.5248

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

Gridchyn, I., &; Kolmogorov, V. (2013). Potts model, parametric maxflow and k-submodular functions (pp. 2320–2327). Presented at the ICCV: International Conference on Computer Vision, Sydney, Australia: IEEE.https://doi.org/10.1109/ICCV.2013.288 
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 (Vol. 7965, pp. 625–636). Presented at the ICALP: Automata, Languages and Programming, Riga, Latvia: Springer.https://doi.org/10.1007/978-3-642-39206-1_53 
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. ACM.https://doi.org/10.1145/2450142.2450146 
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 (Vol. 31, pp. 161–169). Presented at the  AISTATS: Conference on Uncertainty in Artificial Intelligence, Scottsdale, AZ, United States: JMLR.
LibreCat: https://research-explorer.ista.ac.at/record/2901

Takhanov, R., &; Kolmogorov, V. (2013). Inference algorithms for pattern-based CRFs on sequence data. In ICML’13 Proceedings of the 30th International Conference on International (Vol. 28, pp. 145–153). Atlanta, GA, USA: ML Research Press.
LibreCat: https://research-explorer.ista.ac.at/record/2272

Dziembowski, S., Faust, S., Kolmogorov, V., &; Pietrzak, K. Z. (2013). Proofs of Space. IST Austria.
LibreCat: https://research-explorer.ista.ac.at/record/2274

Huber, A., &; Kolmogorov, V. (2012). Towards minimizing k-submodular functions (Vol. 7422, pp. 451–462). Presented at the ISCO: International Symposium on Combinatorial Optimization, Athens, Greece: Springer.https://doi.org/10.1007/978-3-642-32147-4_40 
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. 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. IEEE.https://doi.org/10.1109/TPAMI.2012.105 
LibreCat: https://research-explorer.ista.ac.at/record/2931
DOI: 10.1109/TPAMI.2012.105

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