Research Papers

  • Alexis Espinoza, Amanda Salinas-Pinto, Dorit S. Hochbaum, Julio Godoy, Roberto Asín-Achá. Automatic Algorithm Selection for the Capacitated Vehicle Routing Problem with Given Computational Time Limits. Submitted, Computers and Industrial Engineering
  • Amanda Salinas-Pinto, Catalina Pezo-Vergara, Dorit Hochbaum, Bistra Dilkina, Ricardo Ñanculef, Roberto Asín-Achá. Probing Features for Automatic Algorithm Selection for Pseudo-Boolean Optimization. CPAIOR 2026, to appear
  • Bryan Alvarado-Ulloa, Dorit Hochbaum, Bistra Dilkina, Ricardo Ñanculef, Roberto Asín-Achá. Backbone-based Predict and Search for Pseudo-Boolean Optimization. CPAIOR 2026, to appear
  • Philipp Baumann, Olivier Goldschmidt, Dorit S. Hochbaum, Jason Yang. A Fast and Effective Method for Euclidean Anticlustering: The Assignment-Based-Anticlustering Algorithm. arXiv:2601.06351 https://doi.org/10.48550/arXiv.2601.06351
  • Philipp Baumann, Olivier Goldschmidt, and Dorit S. Hochbaum. A Fast Algorithm for Euclidean Maximum Weight Non-bipartite Matching. In Proceedings of the 15th International Conference on Pattern Recognition Applications and Methods, ISBN 978-989-758-797-9, ISSN 2184-4313, pages 411-418 (2026)
  • Matías Francia-Carraminana, Bryan Alvarado-Ulloa, Dorit S. Hochbaum, Bistra Dilkina, Ricardo Nanculef and Roberto Asín-Achá. Backbones in Pseudo-Boolean Optimization: Extraction and Analysis. ICAART 2026 proceedings, to appear.
  • Dorit S. Hochbaum, Ayleen Irribarra-Cortés, Olivier Goldschmidt and Roberto Asín-Achá. Fast and Optimal Incremental Parametric Procedure for the Densest Subgraph Problem: An Experimental Study. Submitted to SIAM J Optimization, 2025.
  • Dorit S. Hochbaum, Ayleen Irribarra-Cortés, and Roberto Asín-Achá. Faster and Better Quality Conductance Solutions with the Incremental Parametric Cut Algorithm. Submitted to Optimization and Engineering, 2025.
  • Dorit S. Hochbaum, Ayleen Irribarra-Cortés, Olivier Goldschmidt, and Roberto Asín-Achá. Fast and optimal incremental parametric procedure for the densest subgraph problem: An experimental study. arXiv:2509.14993 https://doi.org/10.48550/arXiv.2509.14993 (2025)
  • Dorit Hochbaum and Torpong Nitayanont. An Effective Flow-based Method for Positive-Unlabeled Learning: 2-HNC. arXiv:2505.08212 [cs.LG] , May 2025 https://doi.org/10.48550/arXiv.2505.08212
  • Dorit Hochbaum and Torpong Nitayanont, Confidence HNC: A network flow technique for binary classification with noisy labels. Optimization and Engineering, 1-34 (2025). https://doi.org/10.1007/s11081-025-10014-z Early version, arXiv:2503.02352 [cs.LG], Mar (2025).
  • Dorit S. Hochbaum, Philipp Baumann, Olivier Goldschmidt, Yiqing Zhang. A Fast and Effective Breakpoints Heuristic Algorithm for the Quadratic Knapsack Problem European Journal of Operational Research, 323 (2025) 425–440. Preprint at, arXiv:2408.12183 [math.OC] Aug 2024.
  • Philipp Baumann and Dorit S. Hochbaum. An algorithm for clustering with confidence-based must-link and cannot-link constraints. INFORMS Journal on Computing, Oct (2024) https://doi.org/10.1287/ijoc.2023.0419
  • Catalina Pezo, Dorit S. Hochbaum, Julio Godoy, Roberto Asín-Achá. Automatic Algorithm Selection for Pseudo-Boolean Optimization with Given Computational Time Limits. Computers and Operations Research, Vol. 173, January (2025). https://doi.org/10.1016/j.cor.2024.106836
  • Tor Nitayanont Dorit S. Hochbaum. Positive-Unlabeled Learning Using Pairwise Similarity and Parametric Minimum Cuts, Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2024) – Volume 1: KDIR 2024, pages 60-71, ISBN: 978-989-758-716-0; ISSN: 2184-3228 (2024)
  • Dorit S. Hochbaum. Flow is Best, Fast and Scalable: The Incremental Parametric Cut for Maximum Density and other Ratio Subgraph Problems. Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2024) – Volume 1: KDIR 2024, pages 275-282.
  • Amanda Salinas, Bryan Alvarado, Dorit Hochbaum, Matías Francia, Ricardo Ñanculef, Roberto Asín-Achá. Text-Based Feature-Free Automatic Algorithm Selection. Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2024) – Volume 1: KDIR 2024, pages 267-274.
  • Roberto Asín-Achá, Alexis Espinoza, Olivier Goldschmidt, Dorit S. Hochbaum, Isaías I. Huerta. Selecting Fast Algorithms for the Capacitated Vehicle Routing Problem with Machine Learning Techniques. Networks, 84(4), https://doi.org/10.1002/net.22244 (2024), pages 465-480
  • Nitayanont, T., Lu, C. and Hochbaum, D. (2024). Path of Solutions for Fused Lasso Problems. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods – ICPRAM; ISBN 978-989-758-684-2; ISSN 2184-4313, SciTePress, pages 107-118. DOI: 10.5220/0012433200003654
  • Dorit S. Hochbaum . Unified New Techniques for NP-hard Budgeted Problems with Applications in Team Collaboration, Pattern Recognition, Document Summarization, Community Detection and Imaging. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management – Volume 1: KDIR Nov 2023 ISBN 978-989-758-671-2, ISSN 2184-3228, pages 365-372. DOI: 10.5220/0012207200003598
  • Catalina Pezo, Dorit Hochbaum, Julio Godoy, Roberto Asin-Acha. Automatic Algorithm Selection for Pseudo-Boolean Optimization with Given Computational Time Limits. arXiv arXiv:2309.03924 Sep 2023. https://doi.org/10.48550/arXiv.2309.03924
  • Dorit S. Hochbaum, Zhihao Liu and Olivier Goldschmidt. A Breakpoints-Based Method for the Maximum Diversity and Dispersion Problems. In SIAM Conference on Applied and Computational Discrete Algorithms, pages 189–200, 2023. ACDA 2023. doi:10.1137/1.9781611977714.17
  • Asín-Achá, R.; Goldschmidt, O.; Hochbaum, D. and Huerta, I. Fast Algorithms for the Capacitated Vehicle Routing Problem using Machine Learning Selection of Algorithm’s Parameters. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management – Volume 1: KDIR (2022), pages 29-39. doi: 10.5220/0011405400003335