Publications

Afshani, P. & Cheng, P. (2020). 2D generalization of fractional cascading on axis-aligned planar subdivisions. In Proceedings - 2020 IEEE 61st Annual Symposium on Foundations of Computer Science, FOCS 2020 (pp. 716-727). Article 9317953 IEEE. https://doi.org/10.1109/FOCS46700.2020.00072

Afshani, P., van Duijn, I., Killmann, R. & Nielsen, J. S. (2020). A lower bound for jumbled indexing. In S. Chawla (Ed.), Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (pp. 592-606). Association for Computing Machinery. https://doi.org/10.1137/1.9781611975994.36

Berglin, E. & Brodal, G. S. (2020). A Simple Greedy Algorithm for Dynamic Graph Orientation. Algorithmica, 82(2), 245-259. https://doi.org/10.1007/s00453-018-0528-0

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

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

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

Schmidt, M., Schwiegelshohn, C. & Sohler, C. (2020). Fair Coresets and Streaming Algorithms for Fair k-means. In E. Bampis & N. Megow (Eds.), Approximation and Online Algorithms - 17th International Workshop, WAOA 2019, Revised Selected Papers (pp. 232-251). Springer. https://doi.org/10.1007/978-3-030-39479-0_16

Brodal, G. S., Sioutas, S., Tsakalidis, K. & Tsichlas, K. (2020). Fully persistent B-trees. Theoretical Computer Science, 841, 10-26. https://doi.org/10.1016/j.tcs.2020.06.027

Moeslund, J. E. & Helsing, F., (2020). Indsamlingsforbud for danske dagsommerfugle, 13 p., Notat fra DCE - Nationalt Center for Miljø og Energi Vol. 2020 No. 24 https://dce.au.dk/fileadmin/dce.au.dk/Udgivelser/Notatet_2020/N2020_24.pdf

Larsen, K. G., Simkin, M. & Yeo, K. (2020). Lower Bounds for Multi-server Oblivious RAMs. In R. Pass & K. Pietrzak (Eds.), Theory of Cryptography - 18th International Conference, TCC 2020, Proceedings (pp. 486-503). Springer. https://doi.org/10.1007/978-3-030-64375-1_17

Larsen, K. G., Malkin, T., Weinstein, O. & Yeo, K. (2020). Lower Bounds for Oblivious Near-Neighbor Search. In S. Chawla (Ed.), SODA '20: Proceedings of the Thirty-First Annual ACM-SIAM Symposium on Discrete Algorithms (pp. 1116-1134). Society for Industrial and Applied Mathematics. https://doi.org/10.5555/3381089.3381157

Moeslund, J. E., Nygaard, B. & Ejrnæs, R. (2020). Manual til rødlistevurdering af danske arter 2020-2030. Aarhus Universitet, DCE - Nationalt Center for Miljø og Energi. Teknisk rapport No. 188 https://dce2.au.dk/pub/tr188.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) https://proceedings.neurips.cc/paper/2020/hash/146f7dd4c91bc9d80cf4458ad6d6cd1b-Abstract.html

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

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

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

Larsen, K. G., Mitzenmacher, M. & Tsourakakis, C. E. (2020). Optimal Learning of Joint Alignments with a Faulty Oracle. In 2020 IEEE International Symposium on Information Theory, ISIT 2020 (pp. 2492-2497). IEEE. https://doi.org/10.1109/ISIT44484.2020.9174310

Zardbani, F., Afshani, P. & Karras, P. (2020). Revisiting the theory and practice of database cracking. In A. Bonifati, Y. Zhou, M. A. Vaz Salles, A. Bohm, D. Olteanu, G. Fletcher, A. Khan & B. Yang (Eds.), Advances in Database Technology - EDBT 2020: 23rd International Conference on Extending Database Technology, Proceedings (pp. 415-418). openproceedings.org. https://doi.org/10.5441/002/edbt.2020.46

Larsen, K. G. & Simkin, M. (2020). Secret sharing lower bound: Either reconstruction is hard or shares are long. In C. Galdi & V. Kolesnikov (Eds.), Security and Cryptography for Networks (pp. 566-578). Springer. https://doi.org/10.1007/978-3-030-57990-6_28

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

Anagnostopoulos, A., Becchetti, L., Fazzone, A., Menghini, C. & Schwiegelshohn, C. (2020). Spectral Relaxations and Fair Densest Subgraphs. In CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management (pp. 35–44) https://doi.org/10.1145/3340531.3412036

Sporbert, M., Keil, P., Seidler, G., Bruelheide, H., Jandt, U., Aćić, S., Biurrun, I., Campos, J. A., Čarni, A., Chytrý, M., Ćušterevska, R., Dengler, J., Golub, V., Jansen, F., Kuzemko, A., Lenoir, J., Marceno, C., Moeslund, J. E., Pérez-Haase, A. ... Welk, E. (2020). Testing macroecological abundance patterns: the relationship between local abundance and range size, range position and climatic suitability among European vascular plants. Journal of Biogeography, 47(10), 2210-2222. https://doi.org/10.1111/jbi.13926

Moeslund, J. E. & Helsing, F., (2020). Vurdering af WWF’s anbefalinger om at udvide indsamlingsforbud af dagsommerfugle i Danmark, 5 p., Fagligt notat fra DCE – Nationalt Center for Miljø og Energi Vol. 2020 No. 54 https://dce.au.dk/fileadmin/dce.au.dk/Udgivelser/Notatet_2020/N2020_54.pdf

Moeslund, J. E. & Helsing, F., (2020). Vurdering og genvurdering af effekten af et indsamlingsforbud på bestemte dagsommerfuglearter, 6 p., Fagligt notat fra DCE – Nationalt Center for Miljø og Energi Vol. 2020 No. 47 https://dce.au.dk/fileadmin/dce.au.dk/Udgivelser/Notatet_2020/N2020_47.pdf

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://epubs.siam.org/doi/pdf/10.1137/1.9781611975482.81

Barnabo, G., Leonardi, S., Fazzone, A. & Schwiegelshohn, C. (2019). Algorithms for fair team formation in online labour marketplaces. The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019, 484-490. https://doi.org/10.1145/3308560.3317587

Afshani, P. (2019). A new lower bound for semigroup orthogonal range searching. In G. Barequet & Y. Wang (Eds.), 35th International Symposium on Computational Geometry, SoCG 2019 Article 3 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.SoCG.2019.3

Sporbert, M., Bruelheide, H., Seidler, G., Keil, P., Jandt, U., Austrheim, G., Biurrun, I., Campos, J. A., Čarni, A., Chytrý, M., Csiky, J., De Bie, E., Dengler, J., Golub, V., Grytnes, J-A., Indreica, A., Jansen, F., Martin Jiroušek, M., Lenoir, J. ... Welk, E. (2019). Assessing sampling coverage of species distribution in biodiversity databases. Journal of Vegetation Science, 30(4), 620-632. https://doi.org/10.1111/jvs.12763

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

Larsen, K. G. (2019). Constructive Discrepancy Minimization with Hereditary L2 Guarantees. In R. Niedermeier & C. Paul (Eds.), 36th International Symposium on Theoretical Aspects of Computer Science (STACS 2019) Article 48 Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH. https://doi.org/10.4230/LIPIcs.STACS.2019.48

Afshani, P., Fagerberg, R., Hammer, D., Jacob, R., Kostitsyna, I., Meyer, U., Penschuck, M. & Sitchinava, N. (2019). Fragile complexity of comparison-based algorithms. In M. A. Bender, O. Svensson & G. Herman (Eds.), 27th Annual European Symposium on Algorithms, ESA 2019 Article 2 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.ESA.2019.2

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

Larsen, K. G., Nelson, J., Huy L Nguyen & Thorup, M. (2019). Heavy Hitters via Cluster-Preserving Clustering. Communications of the ACM, 62(8), 95-100. https://doi.org/10.1145/3339185

Afshani, P. & Phillips, J. M. (2019). Independent range sampling, revisited again. In G. Barequet & Y. Wang (Eds.), 35th International Symposium on Computational Geometry, SoCG 2019 Article 4 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.SoCG.2019.4

Moeslund, J. E., Zlinszky, A., Ejrnæs, R., Brunbjerg, A. K., Bøcher, P. K., Svenning, J. C. & Normand, S. (2019). Light detection and ranging explains diversity of plants, fungi, lichens, and bryophytes across multiple habitats and large geographic extent. Ecological Applications, 29(5), Article e01907. https://doi.org/10.1002/eap.1907

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

Afshani, P., Freksen, C. B., Kamma, L. & Larsen, K. G. (2019). Lower bounds for multiplication via network coding. Leibniz International Proceedings in Informatics, LIPIcs, 132, Article 10. https://doi.org/10.4230/LIPIcs.ICALP.2019.10

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

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

Becchetti, L., Bury, M., Cohen-Addad, V., Grandoni, F. & Schwiegelshohn, C. (2019). Oblivious dimension reduction for k-means: Beyond subspaces and the Johnson-lindenstrauss lemma. Proceedings of the Annual ACM Symposium on Theory of Computing, 1039-1050. https://doi.org/10.1145/3313276.3316318

Munteanu, A., Schwiegelshohn, C., Sohler, C. & Woodruff, D. P. (2019). On coresets for logistic regression. Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI), 267-268. https://doi.org/10.18420/inf2019_37

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

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 https://dce.au.dk/fileadmin/dce.au.dk/Udgivelser/Notater_2019/Status_danske_sommerfugle.pdf

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

Anagnostopoulos, A., Angeletti, F., Arcangeli, F., Schwiegelshohn, C. & Vitaletti, A. (2019). Random projection to preserve patient privacy. CEUR Workshop Proceedings, 2482.

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

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

Afshani, P., Agrawal, M., Doerr, B., Doerr, C., Larsen, K. G. & Mehlhorn, K. (2019). The query complexity of a permutation-based variant of Mastermind. Discrete Applied Mathematics, 260, 28-50. https://doi.org/10.1016/j.dam.2019.01.007

Afshani, P., De Berg, M., Casanova, H., Karsin, B., Lambrechts, C., Sitchinava, N. & Tsirogiannis, C. (2018). An efficient algorithm for the 1D total visibility-index problem and its parallelization. ACM Journal of Experimental Algorithmics, 23, Article 2.3. https://doi.org/10.1145/3209685

Munteanu, A. & Schwiegelshohn, C. (2018). Coresets-Methods and History: A Theoreticians Design Pattern for Approximation and Streaming Algorithms. KI - Kunstliche Intelligenz, 32(1), 37-53. https://doi.org/10.1007/s13218-017-0519-3

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