Publications

Brodal, G. S. & Wild, S. (2023). Funnelselect: Cache-Oblivious Multiple Selection. In I. Li Gortz, M. Farach-Colton, S. J. Puglisi & G. Herman (Eds.), 31st Annual European Symposium on Algorithms, ESA 2023 (pp. 25:1-25:17). Article 25 Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.ESA.2023.25

Brodal, G. S., Rysgaard, C. M., Schou, J. K. R. & Svenning, R. (2023). Space-Efficient Functional Offline-Partially-Persistent Trees with Applications to Planar Point Location. In P. Morin & S. Suri (Eds.), Algorithms and Data Structures: 18th International Symposium, WADS 2023, Montreal, QC, Canada, July 31 – August 2, 2023, Proceedings (pp. 644-659). Springer. https://doi.org/10.1007/978-3-031-38906-1_43

Brodal, G. S. & Wild, S. (2024). Deterministic Cache-Oblivious Funnelselect. In H. L. Bodlaender (Ed.), 19th Scandinavian Symposium on Algorithm Theory, SWAT 2024 Article 17 Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.SWAT.2024.17

Brodal, G. S. (2024). Bottom-Up Rebalancing Binary Search Trees by Flipping a Coin. In A. Z. Broder & T. Tamir (Eds.), 12th International Conference on Fun with Algorithms, FUN 2024 Article 6 Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.FUN.2024.6

Brodal, G. S., Fagerberg, R. & Rysgaard, C. M. (2024). On Finding Longest Palindromic Subsequences Using Longest Common Subsequences. In T. Chan, J. Fischer, J. Iacono & G. Herman (Eds.), 32nd Annual European Symposium on Algorithms, ESA 2024 Article 35 Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.ESA.2024.35

Brodal, G. S. & Rysgaard, C. M. (2025). Pure Binary Finger Search Trees. In I.-O. Bercea & R. Pagh (Eds.), 8th SIAM Symposium on Simplicity of Algorithms, SOSA 2025 (pp. 172-195). Society for Industrial and Applied Mathematics.

Brodal, G. S., Lagogiannis, G. & Tarjan, R. E. (2025). Strict Fibonacci Heaps. ACM Transactions on Algorithms, 21(2), Article 15. https://doi.org/10.1145/3707692

Brodal, G. S. (1997). Worst Case Efficient Data Structures. Department of Computer Science, Aarhus University.

Brodal, G. S. (2025). A Simple Integer Successor-Delete Data Structure. In P. Mutzel & N. Prezza (Eds.), 23rd International Symposium on Experimental Algorithms, SEA 2025 Article 8 Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.SEA.2025.8

Brodal, G. S. (2025). Bottom-up rebalancing binary search trees by flipping a coin. Theoretical Computer Science, 1055, Article 115543. https://doi.org/10.1016/j.tcs.2025.115543

Brodal, G. S., Rysgaard, C. M. & Svenning, R. (2025). Buffered Partially-Persistent External-Memory Search Trees. In A. Benoit, H. Kaplan, S. Wild, S. Wild & G. Herman (Eds.), 33rd Annual European Symposium on Algorithms, ESA 2025 Article 82 Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.ESA.2025.82

Brodal, G. S., Iacono, J., Meyer, U., Sitchinava, N., Goodrich, M. T., Lo, J., Pagan, V. & Svenning, R. (2025). External-Memory Priority Queues with Optimal Insertions. In A. Benoit, H. Kaplan, S. Wild, S. Wild & G. Herman (Eds.), 33rd Annual European Symposium on Algorithms, ESA 2025 Article 5 Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.ESA.2025.5

Brody, J. E. & Larsen, K. G. (2015). Adapt or Die: Polynomial Lower Bounds for Non-Adaptive Dynamic Data Structures. Theory of Computing, 11(19), 471-489. https://doi.org/10.4086/toc.2015.v011a019

Bruelheide, H., Dengler, J., Jiménez-Alfaro, B., Purschke, O., Hennekens, S. M., Chytrý, M., Pillar, V. D., Jansen, F., Kattge, J., Sandel, B., Aubin, I., Biurrun, I., Field, R., Haider, S., Jandt, U., Lenoir, J., Peet, R. K., Peyre, G., Sabatini, F. M. ... Zverev, A. (2019). sPlot – A new tool for global vegetation analyses. Journal of Vegetation Science, 30(2), 161-186. https://doi.org/10.1111/jvs.12710

Brunbjerg, A. K., Bruun, H. H., Moeslund, J. E., Sadler, J. P., Svenning, J.-C. & Ejrnæs, R. (2015). Ecospace - a unified framework for understanding variation in biodiversity. Poster session presented at Biodiversitetssymposiet 2015, Aarhus , Denmark.

Brunbjerg, A. K., Bladt, J., Moeslund, J. E. & Ejrnæs, R. (2015). Naturen på kort - nye værktøjer målretter indsatsen for truede arter. Aktuel Naturvidenskab, (3), 12-15.

Brunbjerg, A. K., Bladt, J., Brink, M., Fredshavn, J. R., Mikkelsen, P., Moeslund, J. E., Nygaard, B., Skov, F. & Ejrnæs, R. (2016). Development and implementation of a High Nature Value (HNV) farming indicator for Denmark. Ecological Indicators, 61(2), 274-281. https://doi.org/10.1016/j.ecolind.2015.09.027

Brunbjerg, A. K., Bruun, H. H., Moeslund, J. E., Sadler, J. P., Svenning, J.-C. & Ejrnæs, R. (2017). Ecospace: a unified framework for understanding variation in terrestrial biodiversity. Basic and Applied Ecology, 18, 86-94. https://doi.org/10.1016/j.baae.2016.09.002

Brunbjerg, A. K., Bruun, H. H., Dalby, L., Classen, A. T., Fløjgaard, C., Frøslev, T. G., Hansen, O. L. P., Høye, T. T., Moeslund, J. E., Svenning, J.-C. & Ejrnæs, R. (2020). Multi-taxon inventory reveals highly consistent biodiversity responses to ecospace variation. Oikos, 129(9), 1381-1392. https://doi.org/10.1111/oik.07145

Brunbjerg, A. K., Fløjgaard, C., Frøslev, T. G., Andersen, D. K., Bruun, H. H., Dalby, L., Goldberg, I., Juhl Lehmann, L., Moeslund, J. E. & Ejrnæs, R. (2022). Scrub encroachment promotes biodiversity in temperate European wetlands under eutrophic conditions. Ecology and Evolution, 12(11), Article e9445. https://doi.org/10.1002/ece3.9445

Bucarelli, M. S., Larsen, M. F., Schwiegelshohn, C. & Toftrup, M. B. (2023). On Generalization Bounds for Projective Clustering.

Bucarelli, M. S., Larsen, M. F., Schwiegelshohn, C. & Toftrup, M. B. (2023). On Generalization Bounds for Projective Clustering. In Advances in Neural Information Processing Systems https://papers.nips.cc/paper_files/paper/2023/hash/e30bf4765ae6b16a87fb4d7b0b3b3dec-Abstract-Conference.html

Burkhardt, J., Caragiannis, I., Fehrs, K., Russo, M., Schwiegelshohn, C. & Shyam, S. (2024). Low-Distortion Clustering with Ordinal and Limited Cardinal Information. Proceedings of the AAAI Conference on Artificial Intelligence, 38(9), 9555-9563. https://doi.org/10.1609/aaai.v38i9.28811

Burkhardt, J., Keller, H. J., Orlandi, C. & Schwiegelshohn, C. (2025). Distributed Differentially Private Data Analytics via Secure Sketching. In Forty-second International Conference on Machine Learning: ICML 2025 Article 10733 https://openreview.net/forum?id=2Snksn3U47

Bury, M., Schwiegelshohn, C. & Sorella, M. (2020). Similarity Search for Dynamic Data Streams. IEEE Transactions on Knowledge and Data Engineering, 32(11), 2241-2253. https://doi.org/10.1109/TKDE.2019.2916858

Bury, M., Gentili, M., Schwiegelshohn, C. & Sorella, M. (2021). Polynomial Time Approximation Schemes for All 1-Center Problems on Metric Rational Set Similarities. Algorithmica, 83(5), 1371-1392. https://doi.org/10.1007/s00453-020-00787-3

Bury, M., Grigorescu, E., McGregor, A., Monemizadeh, M., Schwiegelshohn, C., Vorotnikova, S. & Zhou, S. (2019). Structural Results on Matching Estimation with Applications to Streaming. Algorithmica, 81(1), 367-392. https://doi.org/10.1007/s00453-018-0449-y

Bury, M., Schwiegelshohn, C. & Sorella, M. (2018). Sketch 'em all: Fast approximate similarity search for dynamic data streams. WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining, 72-80. https://doi.org/10.1145/3159652.3159694

Caragiannis, I., Larsen, K. G. & Shyam, S. (2025). A New Lower Bound for Multicolor Discrepancy with Applications to Fair Division. In R. Lavi & J. Zhang (Eds.), Algorithmic Game Theory: 18th International Symposium, SAGT 2025, Bath, UK, September 2–5, 2025, Proceedings (pp. 228-246). Springer. https://doi.org/10.1007/978-3-032-03639-1_13

Caspersen, M. E., Bennedsen, J. & Larsen, K. D. (2007). Mental Models and Programming Aptitude. S I G C S E Bulletin Inroads, 39(3), 206-210. https://doi.org/10.1145/1269900.1268845

Chakraborty, D., Kamma, L. & Larsen, K. G. (2018). Tight cell probe bounds for succinct boolean matrix-Vector multiplication. In STOC 2018 - Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing (pp. 1297-1306). Association for Computing Machinery. https://doi.org/10.1145/3188745.3188830

Chan, T. M., Larsen, K. G. & Patrascu, M. (2011). Orthogonal Range Searching on the RAM, Revisited. In Proceedings of the 27th annual ACM symposium on Computational geometry (pp. 1-10). Association for Computing Machinery. https://doi.org/10.1145/1998196.1998198

Chan, T. M., Durocher, S., Larsen, K. G., Morrison, J. & Wilkinson, B. T. (2012). Linear-Space Data Structures for Range Mode Query in Arrays. Leibniz International Proceedings in Informatics, 14, 290-301. https://doi.org/10.4230/LIPIcs.STACS.2012.290

Chan, T. M., Durocher, S., Skala, M. & Wilkinson, B. T. (2012). Linear-Space Data Structures for Range Minority Query in Arrays.

Chan, T. M. & Wilkinson, B. T. (2011). Bichromatic Line Segment Intersection Counting in O(n sqrt{log n}) Time. Paper presented at Canadian Conference on Computational Geometry, Canada. http://www.cccg.ca/proceedings/2011/papers/paper83.pdf

Chan, T. M., Durocher, S., Larsen, K. G., Morrison, J. & Wilkinson, B. T. (2014). Linear-Space Data Structures for Range Mode Query in Arrays. Theory of Computing Systems, 55(4), 719-741. https://doi.org/10.1007/s00224-013-9455-2

Chan, T. M. & Wilkinson, B. T. (2013). Adaptive and Approximate Orthogonal Range Counting. In S. Khanna (Ed.), Proceedings of the Twenty-Fourth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2013 (pp. 241-251). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611973105.18

Chan, T. M., Durocher, S., Skala, M. & Wilkinson, B. T. (2015). Linear-Space Data Structures for Range Minority Query in Arrays. Algorithmica, 72(4), 901-913. https://doi.org/10.1007/s00453-014-9881-9

Chan, T. M. & Wilkinson, B. T. (2016). Adaptive and approximate orthogonal range counting. ACM Transactions on Algorithms, 12(4), 45:1-45:15. Article 45. https://doi.org/10.1145/2830567

Cheng, P. & Afshani, P. (2023). An Optimal Lower Bound for Simplex Range Reporting. In T. Kavitha & K. Mehlhorn (Eds.), 6th Symposium on Simplicity in Algorithms (SOSA 2023) (pp. 272-277). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611977585.ch25

Chung, E. & Larsen, K. G. (2023). Stronger 3SUM-Indexing Lower Bounds. In N. Bansal & V. Nagarajan (Eds.), Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) (pp. 444-455). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611977554.ch19

Chytrý, M., Hennekens, S. M., Jiménez-Alfaro, B., Knollová, I., Dengler, J., Jansen, F., Landucci, F., Schaminée, J. H. J., Aćić, S., Agrillo, E., Ambarlı, D., Angelini, P., Apostolova, I., Attorre, F., Berg, C., Bergmeier, E., Biurrun, I., Botta-Dukát, Z., Brisse, H. ... Yamalov, S. (2015). European Vegetation Archive (EVA): an integrated database of European vegetation plots. Applied Vegetation Science, 19(1), 173-180. https://doi.org/10.1111/avsc.12191

Clifford, R., Jørgensen, A. G. & Larsen, K. G. (2015). New Unconditional Hardness Results for Dynamic and Online Problems. In 56th IEEE Symposium on Foundations of Computer Science (FOCS) Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS IEEE Computer Society Press. http://arxiv.org/pdf/1504.01836v1.pdf

Clifford, R., Grønlund, A., Larsen, K. G. & Starikovskaya, T. (2018). Upper and lower bounds for dynamic data structures on strings. In R. Niedermeier & B. Vallée (Eds.), 35th Symposium on Theoretical Aspects of Computer Science, STACS 2018 (Vol. 96, pp. 22:1-22:14). Article 22 Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.STACS.2018.22

Cohen-Addad, V., Hjuler, N., Parotsidis, N., Saulpic, D. & Schwiegelshohn, C. (2019). Fully dynamic consistent facility location. Advances in Neural Information Processing Systems, 32.

Cohen-Addad, V., Saulpic, D. & Schwiegelshohn, C. (2021). A new coreset framework for clustering. In S. Khuller & V. V. Williams (Eds.), STOC 2021 - Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing (pp. 169-182). Association for Computing Machinery. https://doi.org/10.1145/3406325.3451022

Cohen-Addad, V., Larsen, K. G., Saulpic, D. & Schwiegelshohn, C. (2022). Towards optimal lower bounds for k-median and k-means coresets. In S. Leonardi & A. Gupta (Eds.), STOC 2022 - Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing (pp. 1038-1051). Association for Computing Machinery. https://doi.org/10.1145/3519935.3519946

Cohen-Addad, V., Epasto, A., Lattanzi, S., Mirrokni, V., Munoz Medina, A., Saulpic, D., Schwiegelshohn, C. & Vassilvitskii, S. (2022). Scalable Differentially Private Clustering via Hierarchically Separated Trees. In KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 221-230). Association for Computing Machinery. https://doi.org/10.1145/3534678.3539409

Cohen-Addad, V., Larsen, K. G., Saulpic, D., Schwiegelshohn, C. & Sheikh-Omar, O. A. (2022). Improved Coresets for Euclidean k-Means. arxiv.org. https://doi.org/10.48550/arXiv.2211.08184

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Publications

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