Haller, S, Prakash, M, Hutschenreiter, L, Pietzsch, T, Rother, C, Jug, F, Swoboda, P and Savchynskyy, B (2020). A Primal-Dual Solver for Large-Scale Tracking-by-Assignment. AISTATS 2020

 Tourani, S, Shekhovtsov, A, Rother, C and Savchynskyy, B (2020). Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization. AISTATS 2020. https://gitlab.com/

 Savchynskyy, B (2019). Discrete Graphical Models ? An Optimization Perspective. Foundations and Trends® in Computer Graphics and Vision. Now Publishers. 11 160?429

 Arnab, A, Zheng, S, Jayasumana, S, Romera-paredes, B, Kirillov, A, Savchynskyy, B, Rother, C, Kahl, F and Torr, P (2018). Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation. Cvpr. XX 1?15. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.308.8889&rep=rep1&type=pdf%0Ahttp://dx.doi.org/10.1109/CVPR.2012.6248050

 Shekhovtsov, A, Swoboda, P and Savchynskyy, B (2018). Maximum Persistency via Iterative Relaxed Inference in Graphical Models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40 1668?1682. http://www.icg.tugraz.at/

 Tourani, S, Shekhovtsov, A, Rother, C and Savchynskyy, B (2018). MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11208 LNCS 264?281

 Michel, F, Kirillov, A, Brachmann, E, Krull, A, Gumhold, S, Savchynskyy, B and Rother, C (2017). Global hypothesis generation for 6D object pose estimation. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 2017-Janua 115?124. http://arxiv.org/abs/1612.02287

 Kirillov, A, Levinkov, E, Andres, B, Savchynskyy, B and Rother, C (2017). InstanceCut: From edges to instances with MultiCut. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 2017-Janua 7322?7331

 Kappes, J Hendrik, Swoboda, P, Savchynskyy, B, Hazan, T and Schnörr, C (2016). Multicuts and Perturb & MAP for Probabilistic Graph Clustering. Journal of Mathematical Imaging and Vision. 56 221?237. http://arxiv.org/abs/1601.02088

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

 Kappes, J H, 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 (2015). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. International Journal of Computer Vision. 115 155?184

 Kappes, J H, 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 (2015). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. International Journal of Computer Vision. 115 155?184

 Kappes, J H, 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 (2015). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. International Journal of Computer Vision. 115 155?184. http://hci.iwr.uni-heidelberg.de/opengm2/

 Kirillov, A, Savchynskyy, B, Schlesinger, D, Vetrov, D and Rother, C (2015). Inferring M-best diverse labelings in a single one. Proceedings of the IEEE International Conference on Computer Vision. 2015 Inter 1814?1822

 Kirillov, A, Schlesinger, D, Vetrov, D, Rother, C and Savchynskyy, B (2015). M-best-diverse labelings for submodular energies and beyond. Advances in Neural Information Processing Systems. 2015-Janua 613?621

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

 Kappes, J H, 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 (2014). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. CoRR. abs/1404.0533. http://hci.iwr.uni-heidelberg.de/opengm2/

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

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

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

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

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

 Kappes, J H, Savchynskyy, B and Schnörr, C (2012). A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation. CVPR

 Savchynskyy, B, Schmidt, S, Kappes, J H and Schnörr, C (2012). Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing. UAI 2012

 Schmidt, S, Savchynskyy, B, Kappes, J H and Schnörr, C (2011). Evaluation of a First-Order Primal-Dual Algorithm for MRF Energy Minimization. EMMCVPR. Springer. 6819 89-103