Publications

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

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.

Larsen, K. G. (2024). Bagging is an Optimal PAC Learner (Extended Abstract). In K. Larson (Ed.), Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI-24) (pp. 8411-8415). IJCAI Organization. https://doi.org/10.24963/ijcai.2024/932

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

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

Sølvsten, S., Rysgaard, C. M. & van de Pol, J. (2025). Random Access on Narrow Decision Diagrams in External Memory. In T. Neele & A. Wijs (Eds.), Model Checking Software: 30th International Symposium, SPIN 2024, Luxembourg City, Luxembourg, April 8–9, 2024, Proceedings (pp. 137–145). Springer. https://doi.org/10.1007/978-3-031-66149-5_7

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

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

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

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)

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

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

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

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

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.

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

Grandoni, F., Schwiegelshohn, C., Solomon, S. & Uzrad, A. (2022). Maintaining an EDCS in General Graphs: Simpler, Density-Sensitive and with Worst-Case Time Bounds. In Symposium on Simplicity in Algorithms (SOSA) (pp. 12-23). Society for Industrial and Applied Mathematics Publications. https://doi.org/10.1137/1.9781611977066.2

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

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

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

Larsen, K. G. (2024). From TCS to Learning Theory. In R. Kralovic & A. Kucera (Eds.), 49th International Symposium on Mathematical Foundations of Computer Science, MFCS 2024 Article 4 Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.MFCS.2024.4

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

Leblanc, C., Bonnet, P., Servajean, M., Chytrý, M., Aćić, S., Argagnon, O., Bergamini, A., Biurrun, I., Bonari, G., Campos, J. A., Čarni, A., Ćušterevska, R., De Sanctis, M., Dengler, J., Garbolino, E., Golub, V., Jandt, U., Jansen, F., Lebedeva, M. ... Joly, A. (2024). A deep-learning framework for enhancing habitat identification based on species composition. Applied Vegetation Science, 27(3), Article e12802. https://doi.org/10.1111/avsc.12802

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

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

Brewer, B., Brodal, G. S. & Wang, H. (2024). Dynamic Convex Hulls for Simple Paths. In W. Mulzer & J. M. Phillips (Eds.), 40th International Symposium on Computational Geometry, SoCG 2024 Article 24 Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.SoCG.2024.24

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. & 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

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

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

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

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

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 (2020-...) Vol. 67 https://dce.au.dk/fileadmin/dce.au.dk/Udgivelser/Notater_2023/N2023_67.pdf

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

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., 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.

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

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.

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

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.

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. (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

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.

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.

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

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.

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

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

Toftrup, M., Sørensen, S. A., Ciosici, M. R. & Assent, I. (2021). A reproduction of Apple's bi-directional LSTM models for language identification in short strings. (pp. 36-42).

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

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Publications

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