• 2026

    Research article in proceedings (conference)

    Kononova, A. V., van Stein, N., Mersmann, O., Bäck, T., Bartz-Beielstein, T., Glasmachers, T., Hellwig, M., Krey, S., Kůdela, J., Naujoks, B., Papenmeier, L., Raponi, E., Renau, Q., Rook, J., Schäpermeier, L., Vermetten, D., & Zaharie, D. (2026). Benchmarking that Matters: Rethinking Benchmarking in Continuous Optimisation for Practical Impact. In García-Sánchez, P., Díaz, Á. J., & Murphy, A. (Eds.), Applications of Evolutionary Computation (1st ed., pp. 327–344). Lecture Notes in Computer Science (LNCS): Vol. 16525. Toulouse, Frankreich: Springer Publishing.
    More details BibTeX Full text DOI

    Review article (journal)

    Branke, J., Hutter, F., Pedrielli, G., Poloczek, M., & Papenmeier, L. (2026). Bayesian Optimisation (Dagstuhl Seminar 25451).
    More details Full text DOI

    Research article in digital collection

    Papenmeier, L., & Tighineanu, P. (2026). SMOG: Scalable Meta-Learning for Multi-Objective Bayesian Optimization.
    More details Full text DOI

  • 2025

    Research article in proceedings (conference)

    Cheng, N., Papenmeier, L., Becker, S., & Nardi, L. (2025). A Unified Framework for Entropy Search and Expected Improvement in Bayesian Optimization. In Singh, A., Fazel, M., Hsu, D., Lacoste-Julien, S., Berkenkamp, F., Maharaj, T., Wagstaff, K., & Zhu, J. (Eds.), Proceedings of the 42nd International Conference on Machine Learning (ICML) (pp. 10106–10120). Proceedings of Machine Learning Research: Vol. 267. Vancouver, Canada: MLResearchPress.
    More details BibTeX Full text DOI

    Papenmeier, L., Poloczek, M., & Nardi, L. (2025). Understanding High-Dimensional Bayesian Optimization. In Singh, A., Fazel, M., Hsu, D., Lacoste-Julien, S., Berkenkamp, F., Maharaj, T., Wagstaff, K., & Zhu, J. (Eds.), Proceedings of Machine Learning Research (PMLR) (-, pp. 47902–47923). Proceedings of Machine Learning Research (PMLR): Vol. 267. Vancouver, Canada: MLResearchPress.
    More details BibTeX Full text DOI

    Thesis (doctoral or post-doctoral)

    Papenmeier, L. (2025). Bayesian optimization in high dimensions — a journey through subspaces and challenges. at the Lund University. Lund University, Lund.
    More details BibTeX Full text

    Research article in digital collection (conference)

    Papenmeier, L., Cheng, N., Becker, S., & Nardi, L. (2025). Exploring Exploration in Bayesian Optimization.
    More details Full text DOI

    Research article in digital collection

    Papenmeier, L., & Nardi, L. (2025). Bencher: Simple and Reproducible Benchmarking for Black-Box Optimization.
    More details Full text DOI

  • 2023

    Research article in proceedings (conference)

    Papenmeier, L., Nardi, L., & Poloczek, M. (2023). Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces. In Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., & Levine, S. (Eds.), Advances in Neural Information Processing Systems (-, pp. 1764–1793). Advances in Neural Information Processing Systems: Vol. 36. Red Hook, NY, United States: Curran Associates.
    More details BibTeX Full text

    Research article in digital collection

    Hellsten, E. O., Papenmeier, L., Hvarfner, C., & Nardi, L. (2023). High-dimensional Bayesian Optimization with Group Testing.
    More details Full text DOI

  • 2022

    Research article in proceedings (conference)

    Papenmeier, L., Nardi, L., & Poloczek, M. (2022). Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces. In Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., & Oh, A. (Eds.), Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (-, pp. 11586–11601). Advances in Neural Information Processing Systems: Vol. 35. Red Hook, NY, United States: Curran Associates.
    More details BibTeX Full text

  • 2017

    Research article in digital collection (conference)

    Papenmeier, L., & Friedrich, C. M. (2017). Fasttext and Gradient Boosted Trees at GermEval-2017 on Relevance Classification and Document-level Polarity.
    More details Full text DOI