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A.  Shekhovtsov, Swoboda, P., and Savchynskyy, B., ?Maximum Persistency via Iterative Relaxed Inference in Graphical Models?, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, pp. 1668?1682, 2018.

J. Hendrik Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., ?Multicuts and Perturb & MAP for Probabilistic Graph Clustering?, Journal of Mathematical Imaging and Vision, vol. 56, pp. 221?237, 2016.

P.  Swoboda, Shekhovtsov, A., Kappes, J. Hendrik, Schnörr, C., and Savchynskyy, B., ?Partial Optimality by Pruning for MAP-Inference with General Graphical Models?, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, pp. 1370?1382, 2016.

J. Hendrik Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., ?Probabilistic correlation clustering and image partitioning using perturbed Multicuts?, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9087, pp. 231?242.

P.  Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., ?Partial Optimality by Pruning for MAP-inference with General Graphical Models?, in IEEE Conference on Computer Vision and Pattern Recognition 2014, 2014.

P.  Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., ?Partial Optimality by Pruning for MAP-inference with General Graphical Models?, in IEEE Conference on Computer Vision and Pattern Recognition 2014, 2014.

B.  Savchynskyy, Kappes, J. H., Swoboda, P., and Schnörr, C., ?Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation?, in NIPS, 2013.

B.  Savchynskyy, Kappes, J. Hendrik, Swoboda, P., and Schnörr, C., ?Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation?, in NIPS, 2013.

P.  Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., ?Partial Optimality via Iterative Pruning for the Potts Model?, in Scale Space and Variational Methods (SSVM 2013), 2013.

P.  Swoboda and Schnörr, C., ?Variational Image Segmentation and Cosegmentation with the Wasserstein Distance?, in Energy Minimization Methods in Computer Vision and Pattern Recognition, 2013, vol. 8081, p. 321--334.