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 

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 

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 

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 

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 

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 

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 

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 

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 

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 

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 

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.

Kolmogorov, V., &; Rolinek, M. (2018). Superconcentrators of density 25.3. Ars Combinatoria. Charles Babbage Research Centre.

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 

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 

Kolmogorov, V., Krokhin, A., &; Rolinek, M. (2017). The complexity of general-valued CSPs. SIAM Journal on Computing. SIAM.https://doi.org/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 

Rolinek, M. (2017). Complexity of constraint satisfaction. Institute of Science and Technology Austria.https://doi.org/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 

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 

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 

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 

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.

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 

Barto, L., &; Kazda, A. (2016). Deciding absorption. International Journal of Algebra and Computation. World Scientific Publishing.https://doi.org/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 

Kazda, A. (2016). CSP for binary conservative relational structures. Algebra Universalis. Springer.https://doi.org/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 

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 

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 

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 

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 

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 

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 

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 

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 

Huszár, K., &; Rolinek, M. (n.d.). Playful Math - An introduction to mathematical games. IST Austria.

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.

Kolmogorov, V. (2013). Reweighted message passing revisited. IST Austria.

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 

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 

Kolmogorov, V., &; divný, S. (2013). The complexity of conservative valued CSPs. Journal of the ACM. ACM.https://doi.org/10.1145/2450142.2450146 

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.

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.

Dziembowski, S., Faust, S., Kolmogorov, V., &; Pietrzak, K. Z. (2013). Proofs of Space. IST Austria.

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 

Kolmogorov, V., &; Schoenemann, T. (2012). Generalized sequential tree-reweighted message passing. arXiv. ArXiv.

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 

Kolmogorov, V. (2012). Minimizing a sum of submodular functions. Discrete Applied Mathematics. Elsevier.https://doi.org/10.1016/j.dam.2012.05.025 

Kolmogorov, V. (2012). Generalized roof duality and bisubmodular functions. Discrete Applied Mathematics. Elsevier.https://doi.org/10.1016/j.dam.2011.10.026