P.  Swoboda, Kuske, J., and Savchynskyy, B., ?A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems?, arXiv, preprint, 2016.

J. H.  Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., ?Multicuts and Perturb & MAP for Probabilistic Graph Clustering?, J. Math. Imag. Vision, vol. 56, pp. 221?237, 2016.

P.  Swoboda, Shekhovtsov, A., Kappes, J. H., Schnörr, C., and Savchynskyy, B., ?Partial Optimality by Pruning for MAP-Inference with General Graphical Models?, IEEE Trans. Patt. Anal. Mach. Intell., vol. 38, pp. 1370?1382, 2016.

J. H.  Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., ?A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems?, International Journal of Computer Vision, pp. 1-30, 2015.

J. H.  Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., ?A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems?, Int.~J.~Comp.~Vision, 2015.

J.  Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., ?Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts?, in Proc. SSVM, 2015.

J. H.  Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., ?Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts?, in Proc.~SSVM, 2015.

J. H.  Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., ?A Comparative Study of Modern Inference Techniques for StructuredDiscrete Energy Minimization Problems?, CoRR, 2014.

P.  Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., ?Partial Optimality by Pruning for MAP-inference with General GraphicalModels?, CVPR. Proceedings. pp. 1170-1177, 2014.

B.  Savchynskyy and Schmidt, S., ?Getting Feasible Variable Estimates From Infeasible Ones: MRF LocalPolytope Study?, in Workshop on Inference for Probabilistic Graphical Models at ICCV.Proceedings, 2013.

B.  Savchynskyy, Kappes, J. H., Swoboda, P., and Schnörr, C., ?Global MAP-Optimality by Shrinking the Combinatorial Search Areawith Convex Relaxation?, in NIPS. Proceedings, 2013, pp. 1950-1958.

P.  Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., ?Partial Optimality via Iterative Pruning for the Potts Model?, in Proceedings of the 4th International Conference on Scale Space andVariational Methods in Computer Vision SSVM, 2013, pp. 477-488.

P.  Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., ?Persistency by Pruning for General Graphical Models?, in submitted to NIPS 2013., 2013.

J. H.  Kappes, Savchynskyy, B., and Schnörr, C., ?A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation?, in CVPR. Proceedings, 2012, pp. 1688-1695.

B.  Savchynskyy, Schmidt, S., Kappes, J. H., and Schnörr, C., ?Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing?, UAI. Proceedings, pp. 746-755, 2012.

B.  Savchynskyy and Schmidt, S., ?Getting Feasible Variable Estimates From Infeasible Ones: MRF LocalPolytope Study?, arXiv:1210.4081, 2012.

S.  Schmidt, Savchynskyy, B., Kappes, J. H., and Schnörr, C., ?Evaluation of a First-Order Primal-Dual Algorithm for MRF EnergyMinimization?, in EMMCVPR 2011, 2011, vol. 6819, pp. 89-103.

B.  Savchynskyy, Kappes, J. H., Schmidt, S., and Schnörr, C., ?A Study of Nesterov's Scheme for Lagrangian Decomposition and MAP Labeling?, in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2011.

B.  Savchynskyy, Kappes, J. H., Schmidt, S., and Schnörr, C., ?A Study of Nesterov's Scheme for Lagrangian Decomposition and MAPLabeling?, IEEE International Conference on Computer Vision and Pattern Recognition(CVPR), accepted as oral presentation, pp. 1817 - 1823, 2011.

B.  Savchynskyy, Kappes, J. H., Schmidt, S., and Schnörr, C., ?A Study of Nesterov's Scheme for Lagrangian Decomposition and MAP Labeling?, in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2011.