Publications

Contribution to book anthology

Brodal, G. S., Lagogiannis, G. & Tarjan, R. E. (2012). Strict fibonacci heaps. In STOC '12 Proceedings of the 44th symposium on Theory of Computing (pp. 1177-1184 ). Association for Computing Machinery. https://doi.org/10.1145/2213977.2214082

Brodal, G. S., Nielsen, J. A. S. & Truelsen, J. (2015). Strictly Implicit Priority Queues: On the Number of Moves and Worst-Case Time. In F. Dehne, J-R. Sack & U. Stege (Eds.), Algorithms and Data Structures: 14th International Symposium, WADS 2015, Proceedings (pp. 91-102). Springer VS. https://doi.org/10.1007/978-3-319-21840-3_8

Brodal, G. S., Brodnik, A. & Davoodi, P. (2013). The encoding complexity of two dimensional range minimum data structures. In H. L. Bodlaender & G. F. Italiano (Eds.), Algorithms – ESA 2013: 21st Annual European Symposium, Sophia Antipolis, France, September 2-4, 2013. Proceedings (pp. 229-240). Springer VS. https://doi.org/10.1007/978-3-642-40450-4_20

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. & Katajainen, J. (1998). Worst-case efficient external-memory priority queues. In S. Arnborg & L. Ivansson (Eds.), Algorithm Theory — SWAT'98: 6th Scandinavian Workshop on Algorithm Theory Stockholm, Sweden, July 8–10, 1998 Proceedings (pp. 107-118). Springer. https://doi.org/10.1007/BFb0054359

Brodal, G. S. (1996). Worst-case efficient priority queues. In Proceedings of the seventh annual ACM-SIAM symposium on Discrete algorithms (pp. 52-58). Society for Industrial and Applied Mathematics.

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. & 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., 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

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 Schloss Dagstuhl--Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.STACS.2018.22

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., 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, Inc. https://doi.org/10.1145/3534678.3539409

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

Damgård, I. B., Larsen, K. G. & Yakoubov, S. (2021). Broadcast secret-sharing, bounds and applications. In S. Tessaro (Ed.), 2nd Conference on Information-Theoretic Cryptography, ITC 2021 Article 10 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.ITC.2021.10

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

de Berg, M., Tsirogiannis, C. & Wilkinson, B. T. (2015). Fast computation of categorical richness on raster data sets and related problems. In GIS '15: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Article 18 https://doi.org/10.1145/2820783.2820825

de Berg, M., Tsirogiannis, C. & Wilkinson, B. (2015). Fast Computation of Categorical Richness on Raster Data Sets and Related Problems. In Proceedings. Workshop on Massive Data Algorithmics (MASSIVE) (pp. 86-107)

Eenberg, K., Larsen, K. G. & Yu, H. (2017). DecreaseKeys are expensive for external memory priority queues. In STOC 2017 - Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing (Vol. Part F128415, pp. 1081-1093). Association for Computing Machinery. https://doi.org/10.1145/3055399.3055437

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 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.STACS.2023.31

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

Fleischhacker, N., Larsen, K. G. & Simkin, M. (2022). Property-Preserving Hash Functions for Hamming Distance from Standard Assumptions. In O. Dunkelman & S. Dziembowski (Eds.), Advances in Cryptology – EUROCRYPT 2022: 41st Annual International Conference on the Theory and Applications of Cryptographic Techniques, 2022, Proceedings (pp. 764-781). Springer. https://doi.org/10.1007/978-3-031-07085-3_26

Freksen, C. B. & Larsen, K. G. (2017). On Using Toeplitz and Circulant Matrices for Johnson-Lindenstrauss Transforms. In O. Yoshio & T. Tokuyama (Eds.), 28th International Symposium on Algorithms and Computation (ISAAC 2017) (pp. 32:1-32:12). Article 32 Schloss Dagstuhl--Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.ISAAC.2017.32

Goswami, M., Jørgensen, A. G., Larsen, K. G. & Pagh, R. (2015). Approximate Range Emptiness in Constant Time and Optimal Space. In Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA '15 (pp. 769-775). Society for Industrial and Applied Mathematics. http://dl.acm.org/citation.cfm?id=2133036&picked=prox

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

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

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

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

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

Haxen, M., Raeburn, M., Afshani, P. & Karras, P. (2021). Centerpoint Query Authentication. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM '21) (pp. 3083-3087). Association for Computing Machinery. https://doi.org/10.1145/3459637.3482072

Holt, M. K., Johansen, J. & Brodal, G. S. (2014). On the Scalability of Computing Triplet and Quartet Distances. In C. C. McGeoch & U. Meyer (Eds.), 2014 Proceedings of the Sixteenth Workshop on Algorithm Engineering and Experiments (ALENEX) (pp. 9-19). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611973198.2

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

Jafargholi, Z., Larsen, K. G. & Simkin, M. (2021). Optimal oblivious priority queues. In D. Marx (Ed.), ACM-SIAM Symposium on Discrete Algorithms, SODA 2021 (pp. 2366-2383). Association for Computing Machinery.

Jamalabadi, S., Schwiegelshohn, C. & Schwiegelshohn, U. (2020). Commitment and Slack for Online Load Maximization. In Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures (pp. 339–348). Association for Computing Machinery. https://doi.org/10.1145/3350755.3400271

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

Jørgensen, A. G., Brodal, G. S., Moruz, G., Mølhave, T., Fagerberg, R., Finocchi, I., Grandoni, F. & Italiano, G. F. (2007). Optimal Resilient Dynamic Dictionaries. In L. Arge, M. Hoffmann & E. Welzl (Eds.), Algorithms – ESA 2007: 15th Annual European Symposium, Eilat, Israel, October 8-10, 2007. Proceedings (pp. 347-358). Springer. https://doi.org/10.1007/978-3-540-75520-3_32

Jørgensen, A. G. & Larsen, K. G. (2011). Range Selection and Median: Tight Cell Probe Lower Bounds and Adaptive Data Structures . In Proceedings of the 22nd Annual ACM-SIAM Symposium on Discrete Algorithms. SODA 2011 (pp. 805-813). Society for Industrial and Applied Mathematics. http://www.siam.org/proceedings/soda/2011/SODA11_062_jorgensena.pdf

Kejlberg-Rasmussen, C., Tao, Y., Tsakalidis, K., Tsichlas, K. & Yoon, J. (2013). I/O-Efficient Planar Range Skyline and Attrition Priority Queues. In R. Hull & W. Fan (Eds.), Proceedings of the 32nd symposium on Principles of database systems , PODS '13 (pp. 103-114 ). Association for Computing Machinery. https://doi.org/10.1145/2463664.2465225

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

Larsen, K. G., Pagh, R. & Tetek, J. (2021). CountSketches, Feature Hashing and the Median of Three. In M. Meila & T. Zhang (Eds.), Proceedings of the 38th International Conference on Machine Learning, ICML 2021 (pp. 6011-6020) http://proceedings.mlr.press/v139/larsen21a.html

Larsen, K. G., Weinstein, O. & Yu, H. (2018). Crossing the Logarithmic Barrier for Dynamic Boolean Data Structure Lower Bounds. In M. Henzinger, D. Kempe & I. Diakonikolas (Eds.), STOC 2018 - Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing (pp. 978-989). Association for Computing Machinery. https://doi.org/10.1145/3188745.3188790

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

Larsen, K. G. & Williams, R. (2017). Faster Online Matrix-Vector Multiplication. In P. N. Klein (Ed.), 28th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2017 (Vol. PRDA17, pp. 2182-2189). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611974782

Larsen, K. G., Nelson, J., Nguyen, H. L. & Thorup, M. (2016). Heavy hitters via cluster-preserving clustering. In Proceedings - 57th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2016 (pp. 61-70). Article 7782918 IEEE. https://doi.org/10.1109/FOCS.2016.16

Larsen, K. G. (2012). Higher Cell Probe Lower Bounds for Evaluating Polynomials. In FOCS'12: IEEE 53rd Annual Symposium on Foundations of Computer Science (pp. 293 - 301 ). IEEE Computer Society Press. https://doi.org/10.1109/FOCS.2012.21

Larsen, K. G. & Nguyen, H. L. (2012). Improved Range Searching Lower Bounds. In Proceedings of the 2012 symposuim on Computational Geometry: Chapel Hill, NC, USA — June 17 - 20, 2012 (pp. 171-178). Association for Computing Machinery. https://doi.org/10.1145/2261250.2261275

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

Larsen, K. G. & Walderveen, F. V. (2013). Near-Optimal Range Reporting Structures for Categorical. In Proceedings of the 24th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2013 (pp. 265-276). Association for Computing Machinery. http://knowledgecenter.siam.org/0236-000081/~~PdfSource/0

Larsen, K. G., Munro, J. I., Nielsen, J. A. S. & Thankachan, S. V. (2014). On Hardness of Several String Indexing Problems. In A. Kulikov, S. O. Kuznetsov & P. Pevzner (Eds.), Combinatorial Pattern Matching: 23rd European Symposium on Programming, ESOP 2014, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2014, Grenoble, France, April 5-13, 2014, Proceedings (pp. 242-251 ). Springer VS. https://doi.org/10.1007/978-3-319-07566-2_25

Displaying results 251 to 300 out of 346

Revised 04.10.2023

Department of Computer Science

Publications

Contribution to book anthology

Kasper Green Larsen

Peyman Afshani

Gerth Stølting Brodal

Chris Schwiegelshohn

Publications

Contribution to book anthology

Head of research

Kasper Green Larsen

Faculty

Peyman Afshani

Gerth Stølting Brodal

Chris Schwiegelshohn