Parameter estimation for Gibbs distributions
D.G. Harris, V. Kolmogorov, in:, 50th International Colloquium on Automata, Languages, and Programming, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023.

Solving relaxations of MAP-MRF problems: Combinatorial in-face Frank-Wolfe directions
V. Kolmogorov, in:, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 2023, pp. 11980–11989.

Combining pattern-based CRFs and weighted context-free grammars
R. Takhanov, V. Kolmogorov, Intelligent Data Analysis 26 (2022) 257–272.

Generalized minimum 0-extension problem and discrete convexity
M. Dvorak, V. Kolmogorov, ArXiv (n.d.).

A new notion of commutativity for the algorithmic Lovász Local Lemma
D.G. Harris, F. Iliopoulos, V. Kolmogorov, in:, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, Schloss Dagstuhl - Leibniz Zentrum für Informatik, 2021.

One-sided Frank-Wolfe algorithms for saddle problems
V. Kolmogorov, T. Pock, in:, 38th International Conference on Machine Learning, 2021.

A local lemma for focused stochastical algorithms
D. Achlioptas, F. Iliopoulos, V. Kolmogorov, SIAM Journal on Computing 48 (2019) 1583–1602.

Map inference via block-coordinate Frank-Wolfe algorithm
P. Swoboda, V. Kolmogorov, in:, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 2019.

Function norms for neural networks
A. Rannen-Triki, M. Berman, V. Kolmogorov, M.B. Blaschko, in:, Proceedings of the 2019 International Conference on Computer Vision Workshop, IEEE, 2019.

Testing the complexity of a valued CSP language
V. Kolmogorov, in:, 46th International Colloquium on Automata, Languages and Programming, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2019, p. 77:1-77:12.

Superconcentrators of density 25.3
V. Kolmogorov, M. Rolinek, Ars Combinatoria 141 (2018) 269–304.

Commutativity in the algorithmic Lovász local lemma
V. Kolmogorov, SIAM Journal on Computing 47 (2018) 2029–2056.

Even delta-matroids and the complexity of planar boolean CSPs
A. Kazda, V. Kolmogorov, M. Rolinek, ACM Transactions on Algorithms 15 (2018).

Efficient optimization for rank-based loss functions
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.

The complexity of general-valued CSPs
V. Kolmogorov, A. Krokhin, M. Rolinek, SIAM Journal on Computing 46 (2017) 1087–1110.

Even delta-matroids and the complexity of planar Boolean CSPs
A. Kazda, V. Kolmogorov, M. Rolinek, in:, SIAM, 2017, pp. 307–326.

A faster approximation algorithm for the Gibbs partition function
V. Kolmogorov, in:, Proceedings of the 31st Conference On Learning Theory, ML Research Press, 2017, pp. 228–249.

On the complexity of scrypt and proofs of space in the parallel random oracle model
J.F. Alwen, B. Chen, C. Kamath Hosdurg, V. Kolmogorov, K.Z. Pietrzak, S. Tessaro, in:, Springer, 2016, pp. 358–387.

Inference algorithms for pattern-based CRFs on sequence data
V. Kolmogorov, R. Takhanov, Algorithmica 76 (2016) 17–46.

Total variation on a tree
V. Kolmogorov, T. Pock, M. Rolinek, SIAM Journal on Imaging Sciences 9 (2016) 605–636.