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.

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.

V. Kolmogorov and M. Rolinek, “Superconcentrators of density 25.3,” Ars Combinatoria, vol. 141, no. 10. Charles Babbage Research Centre, pp. 269–304, 2018.

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.

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.

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.

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.

M. Rolinek, “Complexity of constraint satisfaction,” Institute of Science and Technology Austria, 2017.

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.

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.

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.

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.

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.

J. F. Alwen, B. Chen, C. Kamath Hosdurg, V. Kolmogorov, K. Z. Pietrzak, and S. Tessaro, “On the complexity of scrypt and proofs of space in the parallel random oracle model,” presented at the EUROCRYPT: Theory and Applications of Cryptographic Techniques, Vienna, Austria, 2016, vol. 9666, pp. 358–387.

L. Barto and A. Kazda, “Deciding absorption,” International Journal of Algebra and Computation, vol. 26, no. 5. World Scientific Publishing, pp. 1033–1060, 2016.

V. Kolmogorov, T. Pock, and M. Rolinek, “Total variation on a tree,” SIAM Journal on Imaging Sciences, vol. 9, no. 2. Society for Industrial and Applied Mathematics , pp. 605–636, 2016.

A. Kazda, “CSP for binary conservative relational structures,” Algebra Universalis, vol. 75, no. 1. Springer, pp. 75–84, 2016.

V. Kolmogorov, “Commutativity in the algorithmic Lovasz local lemma,” in Proceedings - Annual IEEE Symposium on Foundations of Computer Science, New Brunswick, NJ, USA , 2016, vol. 2016–December.

V. Kolmogorov and R. Takhanov, “Inference algorithms for pattern-based CRFs on sequence data,” Algorithmica, vol. 76, no. 1. Springer, pp. 17–46, 2016.

V. Kolmogorov, M. Rolinek, and R. Takhanov, “Effectiveness of structural restrictions for hybrid CSPs,” in 26th International Symposium, Nagoya, Japan, 2015, vol. 9472, pp. 566–577.

V. Kolmogorov, “A new look at reweighted message passing,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 5. IEEE, pp. 919–930, 2015.

N. Shah, V. Kolmogorov, and C. Lampert, “A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle,” presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, USA, 2015, pp. 2737–2745.

V. Kolmogorov, J. Thapper, and S. divný, “The power of linear programming for general-valued CSPs,” SIAM Journal on Computing, vol. 44, no. 1. SIAM, pp. 1–36, 2015.

V. Kolmogorov, A. Krokhin, and M. Rolinek, “The complexity of general-valued CSPs,” presented at the FOCS: Foundations of Computer Science, Berkeley, CA, United States, 2015, pp. 1246–1258.

S. Dziembowski, S. Faust, V. Kolmogorov, and K. Z. Pietrzak, “Proofs of space,” in 35th Annual Cryptology Conference, Santa Barbara, CA, United States, 2015, vol. 9216, pp. 585–605.

C. Olsson, J. Ulen, Y. Boykov, and V. Kolmogorov, “Partial enumeration and curvature regularization,” presented at the ICCV: International Conference on Computer Vision, Sydney, Australia, 2014, pp. 2936–2943.

K. Huszár and M. Rolinek, Playful Math - An introduction to mathematical games. IST Austria.

Y. Bachrach, P. Kohli, V. Kolmogorov, and M. Zadimoghaddam, “Optimal Coalition Structures in Cooperative Graph Games,” presented at the AAAI: Conference on Artificial Intelligence, Bellevue, WA, United States, 2013, pp. 81–87.

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

I. Gridchyn and V. Kolmogorov, “Potts model, parametric maxflow and k-submodular functions,” presented at the ICCV: International Conference on Computer Vision, Sydney, Australia, 2013, pp. 2320–2327.

V. Kolmogorov, “The power of linear programming for finite-valued CSPs: A constructive characterization,” presented at the ICALP: Automata, Languages and Programming, Riga, Latvia, 2013, vol. 7965, no. 1, pp. 625–636.

V. Kolmogorov and S. divný, “The complexity of conservative valued CSPs,” Journal of the ACM, vol. 60, no. 2. ACM, 2013.

C. Chen, V. Kolmogorov, Z. Yan, D. Metaxas, and C. Lampert, “Computing the M most probable modes of a graphical model,” presented at the  AISTATS: Conference on Uncertainty in Artificial Intelligence, Scottsdale, AZ, United States, 2013, vol. 31, pp. 161–169.

R. Takhanov and V. Kolmogorov, “Inference algorithms for pattern-based CRFs on sequence data,” in ICML’13 Proceedings of the 30th International Conference on International, Atlanta, GA, USA, 2013, vol. 28, no. 3, pp. 145–153.

S. Dziembowski, S. Faust, V. Kolmogorov, and K. Z. Pietrzak, Proofs of Space. IST Austria, 2013.

A. Huber and V. Kolmogorov, “Towards minimizing k-submodular functions,” presented at the ISCO: International Symposium on Combinatorial Optimization, Athens, Greece, 2012, vol. 7422, pp. 451–462.

V. Kolmogorov and T. Schoenemann, “Generalized sequential tree-reweighted message passing,” arXiv. ArXiv, 2012.

L. Torresani, V. Kolmogorov, and C. Rother, “A dual decomposition approach to feature correspondence,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 2. IEEE, pp. 259–271, 2012.

V. Kolmogorov, “Minimizing a sum of submodular functions,” Discrete Applied Mathematics, vol. 160, no. 15. Elsevier, pp. 2246–2258, 2012.

V. Kolmogorov, “Generalized roof duality and bisubmodular functions,” Discrete Applied Mathematics, vol. 160, no. 4–5. Elsevier, pp. 416–426, 2012.

F. Korc, V. Kolmogorov, and C. Lampert, “Approximating marginals using discrete energy minimization,” presented at the ICML: International Conference on Machine Learning, Edinburgh, Scotland, 2012.

F. Korc, V. Kolmogorov, and C. Lampert, Approximating marginals using discrete energy minimization. IST Austria, 2012.