Interactive Optimization and Learning

The IOL research lab is led by Sebastian Pokutta and dedicated to exploring the intersection of mathematical optimization and machine learning, with a focus on developing innovative techniques for learning and optimization. By integrating these two fields, we aim to create new approaches to solving complex problems that leverage the strengths of both optimization and machine learning.

Our group is located both at the Mathematical Optimization research group at the Technische Universität Berlin (TUB) and in the AI in Society, Science, and Technology (AIS²T) department at the Zuse Institute Berlin (ZIB). We are also part of the Berlin mathematics research center MATH+ as well as the Berlin Mathematical School (BMS).

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Zuse Institute

Takustraße 7
14195 Berlin
www.zib.de
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Technische Universität

Str. des 17. Juni 136
10587 Berlin
www.tu.berlin

Upcoming seminar talks

Photo Antonia Chmiela Antonia Chmiela (ZIB)
TBD
@ ZIB, Room 4027

Olivia Röhrig (ZIB)
TBD
@ ZIB, Room 4027

Photo Konrad Mundiger Konrad Mundiger (ZIB)
TBD
@ ZIB, Room 4027

Photo Christophe Roux Christophe Roux (ZIB)
TBD
@ ZIB, Room 4027

Photo Jan Pauls Jan Pauls (Universität Münster)
TBD
@ ZIB, Room 4027

Publication highlights

  1. Parczyk, O., Pokutta, S., Spiegel, C., and Szabó, T. (2024). New Ramsey Multiplicity Bounds and Search Heuristics. Foundations of Computational Mathematics. DOI: 10.1007/s10208-024-09675-6 [arXiv] [code]
    [BibTeX]
    @article{2022_ParczykPokuttaSpiegelSzabo_Ramseymultiplicityheuristics,
  year = {2024},
  journal = {Foundations of Computational Mathematics},
  doi = {10.1007/s10208-024-09675-6},
  archiveprefix = {arXiv},
  eprint = {2206.04036},
  primaryclass = {math.CO},
  author = {Parczyk, Olaf and Pokutta, Sebastian and Spiegel, Christoph and Szabó, Tibor},
  title = {New Ramsey Multiplicity Bounds and Search Heuristics},
  code = {https://zenodo.org/record/6602512#.YyvFhi8Rr5g}
}
  2. Mundinger, K., Pokutta, S., Spiegel, C., and Zimmer, M. (2024). Extending the Continuum of Six-Colorings. Geombinatorics Quarterly, XXXIV. [URL] [arXiv]
    [BibTeX]
    @article{2024_MundingerPokuttaSpiegelZimmer_SixcoloringsExpansion,
  year = {2024},
  journal = {Geombinatorics Quarterly},
  volume = {XXXIV},
  url = {https://geombina.uccs.edu/past-issues/volume-xxxiv},
  archiveprefix = {arXiv},
  eprint = {2404.05509},
  primaryclass = {math.CO},
  author = {Mundinger, Konrad and Pokutta, Sebastian and Spiegel, Christoph and Zimmer, Max},
  title = {Extending the Continuum of Six-Colorings}
}
  3. Mundinger, K., Zimmer, M., and Pokutta, S. (2024). Neural Parameter Regression for Explicit Representations of PDE Solution Operators. [arXiv]
    [BibTeX]
    @misc{2024_MundingerZimmerPokutta_Neuralparameterregression,
  archiveprefix = {arXiv},
  eprint = {2403.12764},
  primaryclass = {cs.LG},
  year = {2024},
  author = {Mundinger, Konrad and Zimmer, Max and Pokutta, Sebastian},
  title = {Neural Parameter Regression for Explicit Representations of PDE Solution Operators}
}
  4. Pauls, J., Zimmer, M., Kelly, U. M., Schwartz, M., Saatchi, S., Ciais, P., Pokutta, S., Brandt, M., and Gieseke, F. (2024). Estimating Canopy Height at Scale. Proceedings of the International Conference on Machine Learning. [arXiv] [code]
    [BibTeX]
    @inproceedings{2024_PaulsEtAl_Canopyheightestimation,
  year = {2024},
  booktitle = {Proceedings of the International Conference on Machine Learning},
  archiveprefix = {arXiv},
  eprint = {2406.01076},
  primaryclass = {cs.CV},
  author = {Pauls, Jan and Zimmer, Max and Kelly, Una M and Schwartz, Martin and Saatchi, Sassan and Ciais, Philippe and Pokutta, Sebastian and Brandt, Martin and Gieseke, Fabian},
  title = {Estimating Canopy Height at Scale},
  code = {https://github.com/AI4Forest/Global-Canopy-Height-Map}
}
  5. Martínez-Rubio, D., Wirth, E., and Pokutta, S. (2023). Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond. Proceedings of the Conference on Learning Theory. [arXiv]
    [BibTeX]
    @inproceedings{2023_MartinezrubioWirthPokutta_Sparseapproximatepagerank,
  year = {2023},
  booktitle = {Proceedings of the Conference on Learning Theory},
  archiveprefix = {arXiv},
  eprint = {2303.12875},
  primaryclass = {math.OC},
  author = {Martínez-Rubio, David and Wirth, Elias and Pokutta, Sebastian},
  title = {Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond}
}
  6. Braun, G., Carderera, A., Combettes, C., Hassani, H., Karbasi, A., Mokhtari, A., and Pokutta, S. (2022). Conditional Gradient Methods. [arXiv]
    [BibTeX]
    @misc{2022_BraunEtAl_Conditionalgradient,
  archiveprefix = {arXiv},
  eprint = {2211.14103},
  primaryclass = {math.OC},
  year = {2022},
  author = {Braun, Gábor and Carderera, Alejandro and Combettes, Cyrille and Hassani, Hamed and Karbasi, Amin and Mokhtari, Aryan and Pokutta, Sebastian},
  title = {Conditional Gradient Methods}
}

Software repositories

All our publically accessible software repositories are available on GitHub. We have a list of actively maintained repositories:

We are also actively involved in the development of the SCIP Optimization Suite at ZIB and its interfaces to other programming languages: