Publications

Hähn, G. J. A., Damasceno, G., Alvarez-Davila, E., Aubin, I., Bauters, M., Bergmeier, E., Biurrun, I., Bjorkman, A. D., Bonari, G., Botta-Dukát, Z., Campos, J. A., Čarni, A., Chytrý, M., Ćušterevska, R., de Gasper, A. L., De Sanctis, M., Dengler, J., Dolezal, J., El-Sheikh, M. A. ... Bruelheide, H. (2025). Global decoupling of functional and phylogenetic diversity in plant communities. Nature Ecology and Evolution, 9(2), 237-248. Article e12976. https://doi.org/10.1038/s41559-024-02589-0

Hájek, M., Jimenez-Alfaro, B., Hájek, O., Brancaleoni, L., Cantonati, M., Carbognani, M., Dedić, A., Dítě, D., Gerdol, R., Hájková, P., Horsáková, V., Jansen, F., Kamberović, J., Kapfer, J., Kolari, T. H. M., Lamentowicz, M., Lazarević, P. M., Mašić, E., Moeslund, J. E. ... Biţă-Nicolae, C. (2021). A European map of groundwater pH and calcium. Earth System Science Data, 13(3), 1089-1105. https://doi.org/10.5194/essd-13-1089-2021

Hanneke, S., Larsen, K. G. & Zhivotovskiy, N. (2024). Revisiting Agnostic PAC Learning. In Proceedings - 2024 IEEE 65th Annual Symposium on Foundations of Computer Science, FOCS 2024 (pp. 1968-1982). IEEE. https://doi.org/10.1109/FOCS61266.2024.00118

Haxen, M., Raeburn, M., Afshani, P. & Karras, P. (2021). Centerpoint Query Authentication. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM '21) (pp. 3083-3087). Association for Computing Machinery. https://doi.org/10.1145/3459637.3482072

Høgsgaard, M. M., Larsen, K. G. & Ritzert, M. (2023). AdaBoost is not an Optimal Weak to Strong Learner. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato & J. Scarlett (Eds.), Proceedings of ICML 2023 (Vol. 202, pp. 13118-13140). MLResearch Press.

Høgsgaard, M. M., Larsen, K. G. & Mathiasen, M. E. (2024). The Many Faces of Optimal Weak-to-Strong Learning. https://arxiv.org/pdf/2408.17148

Høgsgaard, M. M. & Larsen, K. G. (2025). Improved Margin Generalization Bounds for Voting Classifiers. In Proceedings of Thirty Eighth Conference on Learning Theory (Vol. 291, pp. 2822-2855). PMLR. https://proceedings.mlr.press/v291/hogsgaard-moller25a.html

Høgsgaard, M. M., Kamma, L., Larsen, K. G., Nelson, J. & Schwiegelshohn, C. (2024). Sparse Dimensionality Reduction Revisited. In International Conference on Machine Learning (pp. 18454-18469). PMLR.

Høgsgaard, M. M. (2025). Efficient Optimal PAC Learning. In Proceedings of The 36th International Conference on Algorithmic Learning Theory (pp. 578-580). PMLR.

Høgsgaard, M. M. (2025). Guarantees and Insights in Ensemble Learning. [PhD thesis, Aarhus University].

Høgsgaard, M. M. & Paudice, A. (2025). Uniform Mean Estimation for Heavy-Tailed Distributions via Median-of-Means. In Proceedings of the 42nd International Conference on Machine Learning (Vol. 267, pp. 23357-23381)

Holt, M. K., Johansen, J. & Brodal, G. S. (2014). On the Scalability of Computing Triplet and Quartet Distances. In C. C. McGeoch & U. Meyer (Eds.), 2014 Proceedings of the Sixteenth Workshop on Algorithm Engineering and Experiments (ALENEX) (pp. 9-19). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611973198.2

Høye, T. T., Olsen, K., Moeslund, J. E., Strandberg, M. T. & Kjær, C., (2019). Oversigt og status for de danske sommerfuglearter, 8 p., Notat fra DCE - Nationalt Center for Miljø og Energi (2011-2019) https://dce.au.dk/fileadmin/dce.au.dk/Udgivelser/Notater_2019/Status_danske_sommerfugle.pdf

Jacob, R., Larsen, K. G. & Nielsen, J. B. (2019). Lower Bounds for Oblivious Data Structures. In T. M. Chan (Ed.), Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms (pp. 2439-2447). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611975482.149

Jafargholi, Z., Larsen, K. G. & Simkin, M. (2021). Optimal oblivious priority queues. In D. Marx (Ed.), ACM-SIAM Symposium on Discrete Algorithms, SODA 2021 (pp. 2366-2383). Association for Computing Machinery.

Jamalabadi, S., Schwiegelshohn, C. & Schwiegelshohn, U. (2020). Commitment and Slack for Online Load Maximization. In Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures (pp. 339–348). Association for Computing Machinery. https://doi.org/10.1145/3350755.3400271

Jiang, S. & Larsen, K. G. (2019). A Faster External Memory Priority Queue with DecreaseKeys. In T. M. Chan (Ed.), Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms (Vol. PRDA19, pp. 1331-1343). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611975482.81

Johannsen, V. K., Rojas, S. K., Brunbjerg, A. K., Schumacher, J., Bladt, J., Bladt, J., Nyed, P., Moeslund, J. E., Nord-Larsen, T. & Ejrnæs, R. (2015). Udvikling af et High Nature Value - HNV-skovkort for Danmark: IGN Rapport November 2015, Institut for Geovidenskab og Naturforvaltning, Københavns Universitet, Frederiksberg.

Jones, M., Moeslund, J. E., Alexander, C., Bøcher, P. K. & Svenning, J.-C. (2013). Near-ground temperature variability and its predictability in Denmark. Poster session presented at Biodiversitetssymposium 2013, København, Denmark.

Jørgensen, A. G., Brodal, G. S., Moruz, G., Mølhave, T., Fagerberg, R., Finocchi, I., Grandoni, F. & Italiano, G. F. (2007). Optimal Resilient Dynamic Dictionaries. In L. Arge, M. Hoffmann & E. Welzl (Eds.), Algorithms – ESA 2007: 15th Annual European Symposium, Eilat, Israel, October 8-10, 2007. Proceedings (pp. 347-358). Springer. https://doi.org/10.1007/978-3-540-75520-3_32

Jørgensen, A. G., Brodal, G. S. & Mølhave, T. (2009). Fault Tolerant External Memory Algorithms. Lecture Notes in Computer Science, 5664, 411-422. https://doi.org/10.1007/978-3-642-03367-4_36

Jørgensen, A. G. & Brodal, G. S. (2009). Data Structures for Range Median Queries. Lecture Notes in Computer Science, 842-851. https://doi.org/10.1007/978-3-642-10631-6_83

Jørgensen, A. G., Brodal, G. S., Mølhave, T. & Moruz, G. (2009). Counting in the Presence of Memory Faults. Lecture Notes in Computer Science, 5878, 842-851. https://doi.org/10.1007/978-3-642-10631-6_85

Jørgensen, A. G., Greve, M., Larsen, K. D. & Truelsen, J. (2010). Cell Probe Lower Bounds and Approximations for Range Mode. Lecture Notes in Computer Science, 6198, 605-616. https://doi.org/10.1007/978-3-642-14165-2_51

Jørgensen, A. G. & Larsen, K. G. (2011). Range Selection and Median: Tight Cell Probe Lower Bounds and Adaptive Data Structures . In Proceedings of the 22nd Annual ACM-SIAM Symposium on Discrete Algorithms. SODA 2011 (pp. 805-813). Society for Industrial and Applied Mathematics. http://www.siam.org/proceedings/soda/2011/SODA11_062_jorgensena.pdf

Kalavasis, A., Karbasi, A., Larsen, K. G., Velegkas, G. & Zhou, F. (2024). Replicable Learning of Large-Margin Halfspaces. In Proceedings of the 41 st International Conference on Machine Learning (Vol. 235, pp. 22861-22878). MLResearch Press.

Kambach, S., Sabatini, F. M., Attorre, F., Biurrun, I., Boenisch, G., Bonari, G., Čarni, A., Carranza, M. L., Chiarucci, A., Chytrý, M., Dengler, J., Garbolino, E., Golub, V., Güler, B., Jandt, U., Jansen, J., Jašková, A., Jiménez-Alfaro, B., Karger, D. N. ... Bruelheide, H. (2023). Climate-trait relationships exhibit strong habitat specificity in plant communities across Europe. Nature Communications, 14(1), Article 712. https://doi.org/10.1038/s41467-023-36240-6

Karbasi, A. & Larsen, K. G. (2024). The Impossibility of Parallelizing Boosting. In Proceedings of Machine Learning Research (Vol. 237, pp. 635-653)

Karthik, C. S., Lee, E., Rabani, Y., Schwiegelshohn, C. & Zhou, S. (2025). On Approximability of l²₂Min-Sum Clustering. In O. Aichholzer & H. Wang (Eds.), 41st International Symposium on Computational Geometry, SoCG 2025 Article 62 Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.SoCG.2025.62

Kejlberg-Rasmussen, C. & Brodal, G. S. (2012). Cache-Oblivious Implicit Predecessor Dictionaries with the Working-Set Property. Leibniz International Proceedings in Informatics, 14, 112--123. https://doi.org/10.4230/LIPIcs.STACS.2012.112

Kejlberg-Rasmussen, C., Tsakalidis, K. & Tsichlas, K. (2012). I/O-Efficient Dynamic Planar Range Skyline Queries. arxiv.org. http://arxiv.org/abs/1207.2341

Kejlberg-Rasmussen, C., Tao, Y., Tsakalidis, K., Tsichlas, K. & Yoon, J. (2013). I/O-Efficient Planar Range Skyline and Attrition Priority Queues. In R. Hull & W. Fan (Eds.), Proceedings of the 32nd symposium on Principles of database systems , PODS '13 (pp. 103-114 ). Association for Computing Machinery. https://doi.org/10.1145/2463664.2465225

Kejlberg-Rasmussen, C., Kopelowitz, T., Pettie, S. & Porat, E. (2014). Word-packing Algorithms for Dynamic Connectivity and Dynamic Sets. (1407.6755v1 ed.) arxiv.org. http://arxiv.org/abs/1407.6755v1

Kejlberg-Rasmussen, C., Tao, Y., Tsakalidis, K., Tsichlas, K. & Yoon, J. (2021). I/O-efficient 2-d orthogonal range skyline and attrition priority queues. Computational Geometry: Theory and Applications, 93, Article 101689. https://doi.org/10.1016/j.comgeo.2020.101689

Kejlberg-Rasmussen, C. (2013). Dynamic Data Structures: The Interplay of Invariants and Algorithm Design. Department of Computer Science, Aarhus University.

Knollová, I., Chytrý, M., Bruelheide, H., Dullinger, S., Jandt, U., Bernhardt-Römermann, M., Biurrun, I., de Bello, F., Glaser, M., Hennekens, S., Jansen, F., Jiménez-Alfaro, B., Kadaš, D., Kaplan, E., Klinkovská, K., Lenzner, B., Pauli, H., Sperandii, M. G., Verheyen, K. ... Essl, F. (2024). ReSurveyEurope: A database of resurveyed vegetation plots in Europe. Journal of Vegetation Science, 35(2), Article e13235. https://doi.org/10.1111/jvs.13235

Kutz, M., Brodal, G. S., Kaligosi, K. & Katriel, I. (2011). Faster Algorithms for Computing Longest Common Increasing Subsequences. Journal of Discrete Algorithms, 9(4), 314-325. https://doi.org/10.1016/j.jda.2011.03.013

Larsen, K. G. (2011). On Range Searching in the Group Model and Combinatorial Discrepancy. In 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science (FOCS) (pp. 542-549). IEEE Computer Society Press. https://doi.org/10.1109/FOCS.2011.14

Larsen, K. G. (2012). Higher Cell Probe Lower Bounds for Evaluating Polynomials. In FOCS'12: IEEE 53rd Annual Symposium on Foundations of Computer Science (pp. 293 - 301 ). IEEE Computer Society Press. https://doi.org/10.1109/FOCS.2012.21

Larsen, K. G. (2012). The Cell Probe Complexity of Dynamic Range Counting. In STOC’12 : Proceedings of the 44th symposium on Theory of Computing (pp. 85-94). Association for Computing Machinery. https://doi.org/10.1145/2213977.2213987

Larsen, K. G. & Pagh, R. (2012). I/O-Efficient Data Structures for Colored Range and Prefix Reporting. The Annual A C M - S I A M Symposium on Discrete Algorithms. Proceedings, 23, 583-592. http://siam.omnibooksonline.com/2012SODA/index.html

Larsen, K. G. & Nguyen, H. L. (2012). Improved Range Searching Lower Bounds. In Proceedings of the 2012 symposuim on Computational Geometry: Chapel Hill, NC, USA — June 17 - 20, 2012 (pp. 171-178). Association for Computing Machinery. https://doi.org/10.1145/2261250.2261275

Larsen, K. G. & Walderveen, F. V. (2013). Near-Optimal Range Reporting Structures for Categorical. In Proceedings of the 24th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2013 (pp. 265-276). Association for Computing Machinery. http://knowledgecenter.siam.org/0236-000081/~~PdfSource/0

Larsen, K. G., Munro, J. I., Nielsen, J. A. S. & Thankachan, S. V. (2014). On Hardness of Several String Indexing Problems. In A. Kulikov, S. O. Kuznetsov & P. Pevzner (Eds.), Combinatorial Pattern Matching: 23rd European Symposium on Programming, ESOP 2014, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2014, Grenoble, France, April 5-13, 2014, Proceedings (pp. 242-251 ). Springer VS. https://doi.org/10.1007/978-3-319-07566-2_25

Larsen, K. G., Nelson, J. & Nguyen, H. L. (2015). Time Lower Bounds for Nonadaptive Turnstile Streaming Algorithms. In R. Servedio & R. Rubinfeld (Eds.), Proceedings of the Forty-Seventh Annual ACM on Symposium on Theory of Computing, STOC '15 (pp. 803-812). Association for Computing Machinery. https://doi.org/10.1145/2746539.2746542

Larsen, K. G., Munro, J. I., Nielsen, J. S. & Thankachan, S. V. (2015). On hardness of several string indexing problems. Theoretical Computer Science, 582, 74-82. https://doi.org/10.1016/j.tcs.2015.03.026

Larsen, K. G. & Williams, R. (2017). Faster Online Matrix-Vector Multiplication. In P. N. Klein (Ed.), 28th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2017 (Vol. PRDA17, pp. 2182-2189). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611974782

Larsen, K. G., Nelson, J., Nguyen, H. L. & Thorup, M. (2016). Heavy hitters via cluster-preserving clustering. In Proceedings - 57th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2016 (pp. 61-70). Article 7782918 IEEE. https://doi.org/10.1109/FOCS.2016.16

Larsen, K. G. & Nelson, J. (2016). The Johnson-Lindenstrauss Lemma Is Optimal for Linear Dimensionality Reduction. Leibniz International Proceedings in Informatics, 55, 82:1 - 82:11. https://doi.org/10.4230/LIPIcs.ICALP.2016.82

Larsen, K. G. & Nelson, J. (2017). Optimality of the Johnson-Lindenstrauss Lemma. In Proceedings - 58th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2017 (pp. 633-638). Article 8104096 https://doi.org/10.1109/FOCS.2017.64

Displaying results 301 to 350 out of 451

Revised 01.09.2025

Publications

Head of research