Publications

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

Toftrup, M. B., Loeschcke, S., Belongie, S., J. Kastoryano, M. & Snæbjanarson, V. (2024). LoQT: Low-Rank Adapters for Quantized Pretraining. https://arxiv.org/abs/2405.16528

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

Aden-Ali, I., Høgsgaard, M. M., Larsen, K. G. & Zhivotovskiy, N. (2024). Majority-of-Three: The Simplest Optimal Learner? In Proceedings of Thirty Seventh Conference on Learning Theory (Vol. 247, pp. 22-45). PMLR. https://proceedings.mlr.press/v247/aden-ali24a.html

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

Afshani, P. & Cheng, P. (2024). On Semialgebraic Range Reporting. Discrete and Computational Geometry, 71(1), 4-39. https://doi.org/10.1007/s00454-023-00574-1

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

Afshani, P. & Schwiegelshohn, C. (2024). Optimal Coresets for Low-Dimensional Geometric Median. In International Conference on Machine Learning (pp. 262-270). PMLR.

Larsen, K. G., Pagh, R., Persiano, G., Pitassi, T., Yeo, K. & Zamir, O. (2024). Optimal Non-Adaptive Cell Probe Dictionaries and Hashing. In K. Bringmann, M. Grohe, G. Puppis & O. Svensson (Eds.), 51st International Colloquium on Automata, Languages, and Programming, ICALP 2024 Article 104 Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.ICALP.2024.104

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.

Schou, J. K. R. & Wang, B. (2024). PersiSort: A New Perspective on Adaptive Sorting Based on Persistence. In R. I. Nishat (Ed.), Canadian Conference on Computational Geometry: Proceedings of the 36th Canadian Conference on Computational Geometry (CCCG 2024) Brock University, St. Catharines, Canada, July 17 - 19, 2024 (pp. 287-312)

Moeslund, J. E. & Damgaard, C. F. (2024). Predicting Abiotic Soil Characteristics Using Sentinel-2 at Nature-Management-Relevant Spatial Scales and Extents. Remote Sensing, 16(16), Article 3094. https://doi.org/10.3390/rs16163094

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.

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

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

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

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.

Alon, N., Grønlund, A., Jørgensen, S. F. & Larsen, K. G. (2024). Sublinear Time Shortest Path in Expander Graphs. In R. Kralovic & A. Kucera (Eds.), 49th International Symposium on Mathematical Foundations of Computer Science, MFCS 2024 (pp. 8:1-8:13). Article 8 Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.MFCS.2024.8

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

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

Bringmann, K., Grønlund, A., Künnemann, M. & Larsen, K. G. (2024). The NFA Acceptance Hypothesis: Non-Combinatorial and Dynamic Lower Bounds. In V. Guruswami (Ed.), 15th Innovations in Theoretical Computer Science Conference (ITCS 2024) (pp. 22:1-22:25). Article 22 Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.ITCS.2024.22

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

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.

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

Larsen, K. G. (2023). Bagging is an Optimal PAC Learner. In G. Neu & L. Rosasco (Eds.), Proceedings of COLT 2023 (Vol. 195, pp. 450-468). MLResearch Press.

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

Viallat, V. C. A., Grandoni, F., Lee, E. & Schwiegelshohn, C. (2023). Breaching the 2 LMP Approximation Barrier for Facility Location with Applications to k-Median. In Thirty-Fourth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2023) (Vol. 1, pp. 940-986). Association for Computing Machinery.

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

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

Larsen, K. G., Obremski, M. & Simkin, M. (2023). Distributed Shuffling in Adversarial Environments. In K.-M. Chung (Ed.), 4th Conference on Information-Theoretic Cryptography, ITC 2023 Article 10 Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.ITC.2023.10

Tichý, L., Axmanová, I., Dengler, J., Guarino, R., Jansen, F., Midolo, G., Nobis, M. P., Van Meerbeek, K., Aćić, S., Attorre, F., Bergmeier, E., Biurrun, I., Bonari, G., Bruelheide, H., Campos, J. A., Čarni, A., Chiarucci, A., Ćuk, M., Ćušterevska, R. ... Chytrý, M. (2023). Ellenberg-type indicator values for European vascular plant species. Journal of Vegetation Science, 34(1), Article e13168. https://doi.org/10.1111/jvs.13168

Brodal, G. S., Rysgaard, C. M. & Svenning, R. (2023). External Memory Fully Persistent Search Trees. In B. Saha & R. A. Servedio (Eds.), STOC 2023 - Proceedings of the 55th Annual ACM Symposium on Theory of Computing (pp. 1410-1423). Association for Computing Machinery. https://doi.org/10.1145/3564246.3585140

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

Larsen, K. G. (2023). Fast Discrepancy Minimization with Hereditary Guarantees. In N. Bansal & V. Nagarajan (Eds.), Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) (pp. 276-289). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611977554.ch11

Schwiegelshohn, C. (2023). Fitting Data on a Grain of Rice. In I. Chatzigiannakis & I. Karydis (Eds.), Algorithmic Aspects of Cloud Computing: 8th International Symposium, ALGOCLOUD 2023, Amsterdam, The Netherlands, September 5, 2023, Revised Selected Papers (pp. 1-8). Springer. https://doi.org/10.1007/978-3-031-49361-4_13

Peterka, T., Hájková, P., Jiroušek, M., Hinterlang, D., Chytrý, M., Aunina, L., Deme, J., Lyons, M., Seiler, H., Zechmeister, H., Apostolova, I., Beierkuhnlein, C., Bischof, M., Biţă-Nicolae, C., Brancaleoni, L., Ćušterevska, R., Dengler, J., Didukh, Y., Dítě, D. ... Hájek, M. (2023). Formalized classification of the class Montio-Cardaminetea in Europe: towards a consistent typology of spring vegetation. Preslia, 95(3), 347-383. https://doi.org/10.23855/preslia.2023.347

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

Moeslund, J. E., Andersen, D. K., Brunbjerg, A. K., Bruun, H. H., Fløjgaard, C., McQueen, S. N., Nygaard, B. & Ejrnæs, R. (2023). High nutrient loads hinder successful restoration of natural habitats in freshwater wetlands. Restoration Ecology, 31(7), Article e13796. https://doi.org/10.1111/rec.13796

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

Afshani, P. & Cheng, P. (2023). Lower Bounds for Intersection Reporting Among Flat Objects. In E. W. Chambers & J. Gudmundsson (Eds.), 39th International Symposium on Computational Geometry, SoCG 2023 Article 3 Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.SoCG.2023.3

Afshani, P. & Cheng, P. (2023). Lower Bounds for Semialgebraic Range Searching and Stabbing Problems. Journal of the ACM, 70(2), 1-26. Article 16. https://doi.org/10.1145/3578574

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

Afshani, P., Cheng, P., Basu Roy, A. & Wei, Z. (2023). On Range Summary Queries. In K. Etessami, U. Feige & G. Puppis (Eds.), 50th International Colloquium on Automata, Languages, and Programming (ICALP 2023) (pp. 7:1-7:17). Article 7 Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.ICALP.2023.7

Mai, T., Munteanu, A., Musco, C., Rao, A. B., Schwiegelshohn, C. & Woodruff, D. P. (2023). Optimal Sketching Bounds for Sparse Linear Regression. In Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (pp. 11288-11316). PMLR. https://proceedings.mlr.press/v206/mai23a.html

Afshani, P. & Killmann, R. (2023). Rectangle stabbing and orthogonal range reporting lower bounds in moderate dimensions. Computational Geometry: Theory and Applications, 111, Article 101959. https://doi.org/10.1016/j.comgeo.2022.101959

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

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

Larsen, K. G. & Yu, H. (2023). Super-Logarithmic Lower Bounds for Dynamic Graph Problems. In 2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS) (pp. 1589-1604). IEEE. https://doi.org/10.1109/FOCS57990.2023.00096

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.

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