Jan-Hendrik Lange (PhD Student)

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

2023

  1. Article
    D2
    “A Polyhedral Study of Lifted Multicuts,” Discrete Optimization, vol. 47, 2023.

2021

  1. Conference paper
    D2
    “Efficient Message Passing for 0–1 ILPs with Binary Decision Diagrams,” in Proceedings of the 38th International Conference on Machine Learning (ICML 2021), Virtual Event, 2021.

2020

  1. Article
    D2
    “Sparse Recovery with Integrality Constraints,” Discrete Applied Mathematics, vol. 283, 2020.
  2. Conference paper
    D2
    “On the Lifted Multicut Polytope for Trees,” in Pattern Recognition (GCPR 2020), Tübingen, Germany, 2021.
  3. Thesis
    D2
    “Multicut Optimization Guarantees & Geometry of Lifted Multicuts,” Universität des Saarlandes, Saarbrücken, 2020.

2019

  1. Conference paper
    D2
    “Combinatorial Persistency Criteria for Multicut and Max-Cut,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019), Long Beach, CA, USA, 2019.

2018

  1. Conference paper
    D2
    “Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, UT, USA, 2018.
  2. Conference paper
    D2D1
    “Partial Optimality and Fast Lower Bounds for Weighted Correlation Clustering,” in Proceedings of the 35th International Conference on Machine Learning (ICML 2018), Stockholm, Sweden, 2018.

2017

  1. Conference paper
    D2
    “Efficient Algorithms for Moral Lineage Tracing,” in IEEE International Conference on Computer Vision (ICCV 2017), Venice, Italy, 2017.
  2. Conference paper
    D2
    “Analysis and Optimization of Graph Decompositions by Lifted Multicuts,” in Proceedings of the 34th International Conference on Machine Learning (ICML 2017), Sydney, Australia, 2017.