Publications

Cohen-Addad, V., Larsen, K. G., Saulpic, D., Schwiegelshohn, C. & Sheikh-Omar, O. A. (2022). Improved Coresets for Euclidean k-Means. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho & A. Oh (Eds.), Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022 Neural Information Processing Systems Foundation.

Cohen-Addad, V., Saulpic, D. & Schwiegelshohn, C. (2021). Improved Coresets and Sublinear Algorithms for Power Means in Euclidean Spaces. In MA. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang & J. Wortman Vaughan (Eds.), Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021 (pp. 21085-21098). Neural Information Processing Systems Foundation.

Cohen-Addad, V., Saulpic, D. & Schwiegelshohn, C. (2023). Deterministic Clustering in High Dimensional Spaces: Sketches and Approximation. In 2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS) (pp. 1105-1130). IEEE. https://doi.org/10.1109/FOCS57990.2023.00066

Cohen-Addad, V., Draganov, A., Russo, M., Saulpic, D. & Schwiegelshohn, C. (2025). A Tight VC-Dimension Analysis of Clustering Coresets with Applications. In Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2025 (pp. 4783-4808). Association for Computing Machinery.

Cohen-Addad, V., Grandoni, F., Lee, E., Schwiegelshohn, C. & Svensson, O. (2025). A (2+ϵ)-Approximation Algorithm for Metric κ-Median. In M. Koucký & N. Bansal (Eds.), STOC '25: Proceedings of the 57th Annual ACM Symposium on Theory of Computing (pp. 615-624). Association for Computing Machinery. https://doi.org/10.1145/3717823.3718299

Cohen-Addad, V., Lattanzi, S. & Schwiegelshohn, C. (2025). Almost Optimal PAC Learning for k-Means. In M. Koucky & N. Bansal (Eds.), STOC 2025 - Proceedings of the 57th Annual ACM Symposium on Theory of Computing (pp. 2019-2030). Association for Computing Machinery. https://doi.org/10.1145/3717823.3718180

da Cunha, A., Høgsgaard, M. M. & Larsen, K. G. (2024). Optimal Parallelization of Boosting. Abstract from NeurIPS'24: 38th Conference on Neural Information Processing Systems, Vancouver, Canada.

da Cunha, A., Larsen, K. G. & Ritzert, M. (2025). Boosting, Voting Classifiers and Randomized Sample Compression Schemes. In G. Kamath & P. L. Loh (Eds.), Proceedings of Machine Learning Research (Vol. 272, pp. 390-404). MLResearch Press.

Dalsgaard, P., Pedersen, B. P., Dimke, H., Møller, N. M., Normand, S., Bjørk, R., Bille, M. & Larsen, K. G. (2018). Opholdskrav i skatteaftale hæmmer dansk forskning. Politiken, (Sektion 2 (Kultur)), 7.

Damgaard, C., Moeslund, J. E. & Wind, P. (2020). Changes in the abundance of Danish orchids over the past 30 years. Diversity, 12(6), Article 244. https://doi.org/10.3390/d12060244

Damgård, I., Larsen, K. G. & Nielsen, J. B. (2019). Communication Lower Bounds for Statistically Secure MPC, With or Without Preprocessing. In A. Boldyreva & D. Micciancio (Eds.), Advances in Cryptology – CRYPTO 2019 - 39th Annual International Cryptology Conference, Proceedings (Vol. II, pp. 61-84). Springer. https://doi.org/10.1007/978-3-030-26951-7_3

Damgård, I. B., Larsen, K. G. & Yakoubov, S. (2021). Broadcast secret-sharing, bounds and applications. In S. Tessaro (Ed.), 2nd Conference on Information-Theoretic Cryptography, ITC 2021 Article 10 Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.ITC.2021.10

Daniel Kissling, W., Shi, Y., Wang, J., Walicka, A., George, C., Moeslund, J. E. & Gerard, F. (2024). Towards consistently measuring and monitoring habitat condition with airborne laser scanning and unmanned aerial vehicles. Ecological Indicators, 169, Article 112970. https://doi.org/10.1016/j.ecolind.2024.112970

Davoodi, P., Brodal, G. S. & Rao, S. S. (2010). On Space Efficient Two Dimensional Range Minimum Data Structures. Lecture Notes in Computer Science, 6347, Part 2 , 171-182. https://doi.org/10.1007/978-3-642-15781-3_15

Davoodi, P., Brodal, G. S. & Satti, S. R. (2011). Path Minima Queries in Dynamic Weighted Trees. Lecture Notes in Computer Science, 6844 , 290-301. https://doi.org/10.1007/978-3-642-22300-6_25

de Berg, M., Tsirogiannis, C. & Wilkinson, B. T. (2015). Fast computation of categorical richness on raster data sets and related problems. In GIS '15: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Article 18 https://doi.org/10.1145/2820783.2820825

de Berg, M., Tsirogiannis, C. & Wilkinson, B. (2015). Fast Computation of Categorical Richness on Raster Data Sets and Related Problems. In Proceedings. Workshop on Massive Data Algorithmics (MASSIVE) (pp. 86-107)

Di Musciano, M., Zannini, P., Testolin, R., Sabatini, F. M., Santovito, D., Jiménez-Alfaro, B., Jansen, F., Chytrý, M., Ricci, L., Agrillo, E., Attorre, F., Biurrun, I., Bonari, G., Bruun, H. H., Cao Pinna, L., Čarni, A., Carranza, M. L., Cazzolla Gatti, R., Dengler, J. ... Chiarucci, A. (2025). Representativeness of the Natura 2000 network for preserving plant biodiversity in the European Union. Conservation Biology. Advance online publication. https://doi.org/10.1111/cobi.70158

Draganov, A. A., Saulpic, D. & Schwiegelshohn, C. (2024). Settling Time vs. Accuracy Tradeoffs for Clustering Big Data. Proceedings of the ACM on Management of Data, 2(3), Article 173. https://doi.org/10.1145/3654976

Eenberg, K., Larsen, K. G. & Yu, H. (2017). DecreaseKeys are expensive for external memory priority queues. In STOC 2017 - Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing (Vol. Part F128415, pp. 1081-1093). Association for Computing Machinery. https://doi.org/10.1145/3055399.3055437

Ejrnæs, R., Petersen, A. H., Bladt, J., Bruun, H. H., Moeslund, J. E., Wiberg-Larsen, P. & Rahbek, C. (2014). Biodiversitetskort for Danmark: Udviklet i samarbejde mellem Center for Makroøkologi, Evolution og Klima på Københavns Universitet og Institut for Bioscience ved Aarhus Universitet. Aarhus University, DCE - Danish Centre for Environment and Energy. Videnskabelig rapport fra DCE - Nationalt Center for Miljø og Energi No. 112

Ejrnæs, R., Moeslund, J. E. & Bladt, J., (2014). Analyse om omfang af biodiversitet repræsenteret i de udpegede Natura 2000 områder på land, 16 p., Notat fra DCE - Nationalt Center for Miljø og Energi (2011-2019)

Ejrnæs, R., Moeslund, J. E., Brunbjerg, A. K., Groom, G. B. & Bladt, J. (2018). Videreudvikling af lokal bioscore for biodiversitetskortet for Danmark. Aarhus University, DCE - Danish Centre for Environment and Energy. Teknisk rapport fra DCE - Nationalt Center for Miljø og Energi Vol. 122 http://dce2.au.dk/pub/TR122.pdf

Ejrnæs, R., Bladt, J., Moeslund, J. E. & Brunbjerg, A. K. (2021). Biodiversitetskortets bioscore. Aarhus University, DCE - Danish Centre for Environment and Energy. Videnskabelig rapport fra DCE - Nationalt Center for Miljø og Energi No. 456

Ejrnæs, R., Nygaard, B., Kjær, C., Baattrup-Pedersen, A., Brunbjerg, A. K., Clausen, K. K., Fløjgaard, C., Hansen, J. L. S., D.D. Hansen, M., Holm, T. E., Johnsen, T. J., Johansson, L. S., Moeslund, J. E., Sterup, J., Hansen, R. R., Strandberg, B., Søndergaard, M. & Wiberg-Larsen, P. (2021). Danmarks biodiversitet 2020. Aarhus University, DCE - Danish Centre for Environment and Energy. Videnskabelig rapport fra DCE - Nationalt Center for Miljø og Energi No. 465 https://dce2.au.dk/pub/SR465.pdf

Ejrnæs, R., Moeslund, J. E., Fløjgaard, C., Bladt, J. & Brunbjerg, A. K., (2023). Fagligt grundlag for overvågning af naturnationalparker, Fagligt notat fra DCE – Nationalt Center for Miljø og Energi Vol. 67 https://dce.au.dk/fileadmin/dce.au.dk/Udgivelser/Notater_2023/N2023_67.pdf

Ejrnæs, R., Dalby, L., Bladt, J., Søndergaard, S. A., Dümke, L., Fløjgaard, C., Bruun, L. D., Ejrnæs, D. D., Moeslund, J. E. & Bruun, H. H. (2024). Opportunities and barriers for promoting biodiversity in Danish beef production. iScience, 27(12), Article 111422. https://doi.org/10.1016/j.isci.2024.111422

Fandina, O. N., Høgsgaard, M. M. & Larsen, K. G. (2023). Barriers for Faster Dimensionality Reduction. In P. Berenbrink, P. Bouyer, A. Dawar & M. M. Kante (Eds.), 40th International Symposium on Theoretical Aspects of Computer Science, STACS 2023 Article 31 Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.STACS.2023.31

Fandina, O. N., Høgsgaard, M. M. & Larsen, K. G. (2023). The Fast Johnson-Lindenstrauss Transform Is Even Faster. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato & J. Scarlett (Eds.), Proceedings of ICML 2023 (Vol. 202, pp. 9689-9715). MLResearch Press.

Farhadi, A., Hajiaghayi, M. T., Larsen, K. G. & Shi, E. (2019). Lower bounds for external memory integer sorting via network coding. In M. Charikar & E. Cohen (Eds.), STOC 2019 - Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing (pp. 997-1008). Association for Computing Machinery. https://doi.org/10.1145/3313276.3316337

Fleischhacker, N., Larsen, K. G. & Simkin, M. (2022). Property-Preserving Hash Functions for Hamming Distance from Standard Assumptions. In O. Dunkelman & S. Dziembowski (Eds.), Advances in Cryptology – EUROCRYPT 2022: 41st Annual International Conference on the Theory and Applications of Cryptographic Techniques, 2022, Proceedings (pp. 764-781). Springer. https://doi.org/10.1007/978-3-031-07085-3_26

Fleischhacker, N., Larsen, K. G. & Simkin, M. (2023). How to Compress Encrypted Data. In C. Hazay & M. Stam (Eds.), Advances in Cryptology – EUROCRYPT 2023: 42nd Annual International Conference on the Theory and Applications of Cryptographic Techniques, Lyon, France, April 23-27, 2023, Proceedings, Part I (pp. 551-577). Springer. https://doi.org/10.1007/978-3-031-30545-0_19

Fleischhacker, N., Larsen, K. G., Obremski, M. & Simkin, M. (2024). Invertible Bloom Lookup Tables with Less Memory and Randomness. In T. Chan, J. Fischer, J. Iacono & G. Herman (Eds.), 32nd Annual European Symposium on Algorithms, ESA 2024 Article 54 Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.ESA.2024.54

Fløjgaard, C., Frøslev, T. G., Brunbjerg, A. K., Bruun, H. H., Moeslund, J. E., Hansen, A. J. & Ejrnæs, R. (2019). Predicting provenance of forensic soil samples: Linking soil to ecological habitats by metabarcoding and supervised classification. PLoS One, 14(7), Article e0202844. https://doi.org/10.1371/journal.pone.0202844

Fløjgaard, C., Valdez, J. W., Dalby, L., Moeslund, J. E., Clausen, K. K., Ejrnæs, R., Pärtel, M. & Brunbjerg, A. K. (2020). Dark diversity reveals importance of biotic resources and competition for plant diversity across habitats. Ecology and Evolution, 10(12), 6078-6088. https://doi.org/10.1101/685040, https://doi.org/10.1002/ece3.6351

Freksen, C. B. & Larsen, K. G. (2017). On Using Toeplitz and Circulant Matrices for Johnson-Lindenstrauss Transforms. In O. Yoshio & T. Tokuyama (Eds.), 28th International Symposium on Algorithms and Computation (ISAAC 2017) (pp. 32:1-32:12). Article 32 Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.ISAAC.2017.32

Freksen, C. B., Kamma, L. & Larsen, K. G. (2018). Fully Understanding the Hashing Trick. arxiv.org. http://arxiv.org/abs/1805.08539

Freksen, C. B., Kamma, L. & Larsen, K. G. (2018). Fully Understanding the Hashing Trick. 1. Poster session presented at Neural Information Processing Systems Conference, Montreal, Canada.

Freksen, C. B. & Larsen, K. G. (2020). On Using Toeplitz and Circulant Matrices for Johnson–Lindenstrauss Transforms. Algorithmica, 82(2), 338-354. https://doi.org/10.1007/s00453-019-00644-y

Gao, J., Jayaram, R., Kolbe, B., Sapir, S., Schwiegelshohn, C., Silwal, S. & Waingarten, E. (2025). Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures. In Proceedings of the 42nd International Conference on Machine Learning (Vol. 267, pp. 18363-18385)

Goswami, M., Jørgensen, A. G., Larsen, K. G. & Pagh, R. (2015). Approximate Range Emptiness in Constant Time and Optimal Space. In Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA '15 (pp. 769-775). Society for Industrial and Applied Mathematics. http://dl.acm.org/citation.cfm?id=2133036&picked=prox

Grandoni, F., Schwiegelshohn, C., Solomon, S. & Uzrad, A. (2022). Maintaining an EDCS in General Graphs: Simpler, Density-Sensitive and with Worst-Case Time Bounds. In Symposium on Simplicity in Algorithms (SOSA) (pp. 12-23). Society for Industrial and Applied Mathematics Publications. https://doi.org/10.1137/1.9781611977066.2

Green Larsen, K., Mitzenmacher, M. & Tsourakakis, C. (2020). Clustering with a faulty oracle. In Y. Huang, I. King, T.-Y. Liu & M. van Steen (Eds.), WWW '20: Proceedings of The Web Conference 2020 (pp. 2831-2834). Association for Computing Machinery. https://doi.org/10.1145/3366423.3380045

Grønlund, A. & Larsen, K. G. (2016). Towards Tight Lower Bounds for Range Reporting on the RAM. Leibniz International Proceedings in Informatics, 55, 92:1 - 92:12. https://doi.org/10.4230/LIPIcs.ICALP.2016.92

Grønlund, A., Kamma, L., Larsen, K. G., Mathiasen, A. & Nelson, J. (2019). Margin-Based Generalization Lower Bounds for Boosted Classifiers. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox & R. Garnett (Eds.), Advances in Neural Information Processing Systems 32 (NIPS 2019) (Vol. 32). Neural Information Processing Systems Foundation. https://arxiv.org/abs/1909.12518

Grønlund, A., Larsen, K. G. & Mathiasen, A. (2019). Optimal Minimal Margin Maximization with Boosting. In K. Chaudhuri & R. Salakhutdinov (Eds.), 36th International Conference on Machine Learning, ICML 2019 (Vol. 97, pp. 4392-4401). International Machine Learning Society (IMLS). http://proceedings.mlr.press/v97/mathiasen19a/mathiasen19a.pdf

Grønlund, A., Kamma, L. & Larsen, K. G. (2020). Margins are Insufficient for Explaining Gradient Boosting. In H. Larochelle, MA. Ranzato, R. Hadsell, M.-F. Balcan & H.-T. Lin (Eds.), Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (Vol. 2020-December) https://proceedings.neurips.cc/paper/2020/hash/146f7dd4c91bc9d80cf4458ad6d6cd1b-Abstract.html

Grønlund, A., Kamma, L. & Larsen, K. G. (2020). Near-Tight Margin-Based Generalization Bounds for Support Vector Machines. In H. Daumé III & A. Singh (Eds.), International Conference on Machine Learning (pp. 3779-3788). MLResearch Press. http://proceedings.mlr.press/v119/gronlund20a.html

Groom, G. B., Bladt, J., Moeslund, J. E. & Ejrnæs, R. (2018). Developing biodiversity proxies: Technical description. Aarhus University, DCE - Danish Centre for Environment and Energy. Technical Report from DCE – Danish Centre for Environment and Energy No. 123

Gündoğar, Z., Ho̷jlund, M. G., Larsen, K. G. & Christiansen, O. (2025). Recursive Linear Tensor Expansion with Natural Occupation Analysis. Journal of Chemical Theory and Computation, 21(19), 9270-9289. https://doi.org/10.1021/acs.jctc.5c01101

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Publications

Head of research