Jan-Hendrik Lange (PhD Student)

MSc Jan-Hendrik Lange

Address
Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus E1 4
66123 Saarbrücken
Location
E1 4 - Room 629
Phone
+49 681 9325 2029
Fax
+49 681 9325 2099
Email
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Personal Information

Research Interests:

  • Combinatorial Optimization
  • Applications of Mathematical Optimization in Machine Learning

Education:

  • Master of Science in Mathematics with Minor in Computer Science, Technische Universität Darmstadt, June 2016
  • Since June 2016: PhD Student at Max Planck Institute for Informatics

 

Publications

2018
Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs
E. Laude, J.-H. Lange, J. Schüpfer, C. Domokos, L. Leal-Taixé, F. R. Schmidt, B. Andres and D. Cremers
31st IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), 2018
(Accepted/in press)
Partial Optimality and Fast Lower Bounds for Weighted Correlation Clustering
J.-H. Lange, A. Karrenbauer and B. Andres
Proceedings of the 35th International Conference on Machine Learning (ICML 2018), 2018
2017
Efficient Algorithms for Moral Lineage Tracing
M. Rempfler, J.-H. Lange, F. Jug, C. Blasse, E. W. Myers, B. H. Menze and B. Andres
IEEE International Conference on Computer Vision (ICCV 2017), 2017
Analysis and Optimization of Graph Decompositions by Lifted Multicuts
A. Horňáková, J.-H. Lange and B. Andres
Proceedings of the 34th International Conference on Machine Learning (ICML 2017), 2017
Decomposition of Trees and Paths via Correlation
J.-H. Lange and B. Andres
Technical Report, 2017
(arXiv: 1706.06822v2)
Abstract
We study the problem of decomposing (clustering) a tree with respect to costs attributed to pairs of nodes, so as to minimize the sum of costs for those pairs of nodes that are in the same component (cluster). For the general case and for the special case of the tree being a star, we show that the problem is NP-hard. For the special case of the tree being a path, this problem is known to be polynomial time solvable. We characterize several classes of facets of the combinatorial polytope associated with a formulation of this clustering problem in terms of lifted multicuts. In particular, our results yield a complete totally dual integral (TDI) description of the lifted multicut polytope for paths, which establishes a connection to the combinatorial properties of alternative formulations such as set partitioning.