Publications

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

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

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

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

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

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

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

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

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

Caragiannis, I., Larsen, K. G. & Shyam, S. (2026). 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

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

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 (Vol. 267, pp. 5913-5942). Article 10733 https://openreview.net/forum?id=2Snksn3U47

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

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

Brunbjerg, A. K., Bladt, J., Ejrnæs, R., Nygaard, B. & Moeslund, J. E. (2026). Designing a long-term monitoring program for Denmark's national parks. Poster session presented at BioMonWeek, Montpellier, France.

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

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

Brodal, G. S., Demaine, E. D. & Munro, J. I. (2005). Fast Allocation and Deallocation with an Improved Buddy System. Acta Informatica, 41(4-5), 273-291. https://doi.org/10.1007/s00236-004-0159-6

Brodal, G. S., Fagerberg, R. & Moruz, G. (2005). On the Adaptiveness of Quicksort. In ALENEX05 (pp. 130-140). Society for Industrial and Applied Mathematics.

Brodal, G. S., Fagerberg, R. & Moruz, G. (2005). Cache-Aware and Cache-Oblivious Adaptive Sorting. In L. Caires, G. F. Italiano, L. Monteiro, C. Palamidessi & M. Yung (Eds.), Automata, Languages and Programming: 32nd International Colloquium, ICALP 2005, Lisbon, Portugal, July 11-15, 2005. Proceedings (pp. 576-588). Springer. https://doi.org/10.1007/11523468_47

Brodal, G. S. & Moruz, G. (2005). Tradeoffs Between Branch Mispredictions and Comparisons for Sorting Algorithms. In F. Dehne, A. Lopez-Ortiz & J.-R. Sack (Eds.), Algorithms and Data Structures: 9th International Workshop, WADS 2005, Waterloo, Canada, August 15-17, 2005. Proceedings (pp. 385-395). Springer. https://doi.org/10.1007/11534273_34

Brodal, G. S. & Fagerberg, R. (2006). Cache-oblivious String Dictionaries. In SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm (pp. 581-590). Association for Computing Machinery. https://doi.org/10.1145/1109557.1109621

Brodal, G. S. (2005). Finger Search Trees. In D. Mehta & S. Sahni (Eds.), Handbook of Data Structures and Applications CRC Press.

Brodal, G. S. & Leonardi, S. (Eds.) (2005). ESA 2005: 13th Annual European Symposium: Springer LNCS.

Brodal, G. S. (2004). Cache-Oblivious Algorithms and Data Structures. In T. Hagerup & J. Katajainen (Eds.), Algorithm Theory - SWAT 2004: 9th Scandinavian Workshop on Algorithm Theory, Humlebaek, Denmark, July 8-10, 2004. Proceedings (pp. 3-13). Springer. https://doi.org/10.1007/978-3-540-27810-8_2

Displaying results 201 to 250 out of 454

Revised 01.09.2025

Publications

Head of research