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.
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Schrödter, K., Pauls, J., & Gieseke, F. (2026). Canopy Tree Height Estimation using Quantile Regression: Modeling and Evaluating Uncertainty in Remote Sensing. In Proceedings of the Twenty-Ninth Annual Conference on Artificial Intelligence and Statistics (AISTATS), Tangier. (accepted / in press (not yet published))
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Schrödter, K., Stenkamp, J., Herrmann, N., & Gieseke, F. (2026). Trainable Bitwise Soft Quantization for Input Feature Compression. In Proceedings, o. M. L. R. (Ed.), Proceedings of the Third Conference on Parsimony and Learning (CPAL) (pp. 896–920). Tübingen: MLResearchPress.
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Review article (journal)

Branke, J., Hutter, F., Pedrielli, G., Poloczek, M., & Papenmeier, L. (2026). Bayesian Optimisation (Dagstuhl Seminar 25451).
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Research article in digital collection (conference)

Herrmann, N., Stenkamp, J., Karic, B., Oehmke, S., & Gieseke, F. (2026). Boosted Trees on a Diet: Compact Models for Resource-Constrained Devices. (accepted / in press (not yet published))
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Stenkamp, J., Hunke, M., Karatas, C., Kirchhoff, S., Knaden, C., Naebers, P., Zhao, L., Karic, B., Gieseke, F., & Herrmann, N. (2026). Counting Parked Bicycles on the Edge — A TinyML Smart City Application. (accepted / in press (not yet published))
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Research article in digital collection

Papenmeier, L., & Tighineanu, P. (2026). SMOG: Scalable Meta-Learning for Multi-Objective Bayesian Optimization.
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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.
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Fayad, I., Zimmer, M., Schwartz, M., Ciais, P., Gieseke, F., de Truchis, A., Brood, S., Belouze, G., & d'Aspremont, A. (2025). DUNIA: Pixel-Sized Embeddings via Cross-Modal Alignment for Earth Observation Applications. In Proceedings of the 42nd International Conference on Machine Learning (ICML), Vancouver. (accepted / in press (not yet published))
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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.
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Pauls, J., Zimmer, M., Turan, B., Saatchi, S., Ciais, P., Pokutta, S., & Gieseke, F. (2025). Capturing Temporal Dynamics in Large-Scale Tree Canopy Height Estimation. In Proceedings of the 42nd International Conference on Machine Learning (ICML), Vancouver. (accepted / in press (not yet published))
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Research article (journal)

Bernardino, P. N., Keersmaecker, W. D., Horion, S., Kerchove, R. V. D., Lhermitte, S., Fensholt, R., Oehmcke, S., Gieseke, F., Meerbeek, K. V., Abel, C., Verbesselt, J., & Somers, B. (2025). Predictability of abrupt shifts in dryland ecosystem functioning. Nature Climate Change, 15, 86–91.
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Brandt, M., Chave, J., Li, S., Fensholt, R., Ciais, P., Wigneron, J.-P., Gieseke, F., Saatchi, S., Tucker, C. J., & Igel, C. (2025). High-resolution sensors and deep learning models for tree resource monitoring. Nature Reviews Electrical Engineering, 2, 13–26.
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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.
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Research article in digital collection

Benjamin, K., Nina, H., Jan, S., Paula, S., Fabian, G., & Angela, S. (2025). Send Less, Save More: Energy-Efficiency Benchmark of Embedded CNN Inference vs. Data Transmission in IoT.
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Papenmeier, L., & Nardi, L. (2025). Bencher: Simple and Reproducible Benchmarking for Black-Box Optimization.
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Research article in digital collection (conference)

Papenmeier, L., Cheng, N., Becker, S., & Nardi, L. (2025). Exploring Exploration in Bayesian Optimization.
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2024

 

Research article in proceedings (conference)

Lülf, C., Lima Martins, D. M., Vaz, S. M. A., Zhou, Y., & Gieseke, F. (2024). CLIP-Branches: Interactive Fine-Tuning for Text-Image Retrieval. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval (Demo Track), Washington, D.C. (accepted / in press (not yet published))
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Pauls, J., Zimmer, M., Kelly, U. M., Schwartz, M., Saatchi, S., Ciais, P., Pokutta, S., Brandt, M., & Gieseke, F. (2024). Estimating Canopy Height at Scale. In Proceedings of the 41st International Conference on Machine Learning (ICML), Wien. (accepted / in press (not yet published))
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Research article (journal)

Herrmann, N., Dieckmann, J., & Kuchen, H. (2024). Optimizing Three-Dimensional Stencil-Operations on Heterogeneous Computing Environments. International Journal of Parallel Programming, 52(4), 274–297.
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Oehmcke, S., Li, L., Trepekli, K., Revenga, J. C., Nord-Larsen, T., Gieseke, F., & Igel, C. (2024). Deep point cloud regression for above-ground forest biomass estimation from airborne LiDAR. Remote Sensing of Environment, 302.
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