Publications

Contribution to book anthology

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. A C M Symposium on the Theory of Computing. Annual Proceedings No. 2018 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). [22] Schloss Dagstuhl--Leibniz-Zentrum für Informatik. Leibniz International Proceedings in Informatics No. 96 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. Proceedings of the Annual ACM Symposium on Theory of Computing 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. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 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 [10] Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. Leibniz International Proceedings in Informatics, LIPIcs Vol. 199 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. Lecture Notes in Computer Science Vol. 11693 https://doi.org/10.1007/978-3-030-26951-7_3

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 [31] Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. Leibniz International Proceedings in Informatics, LIPIcs Vol. 254 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. A C M Symposium on the Theory of Computing. Annual Proceedings 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. Lecture Notes in Computer Science (LNCS) Vol. 13276 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). [32] Schloss Dagstuhl--Leibniz-Zentrum für Informatik. Leibniz International Proceedings in Informatics Vol. 92 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) 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. Proceedings of Machine Learning Research Vol. 119 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.), Proceedings of th 36th International Conference on Machine Learning (Vol. 97, pp. 4392-4401). International Machine Learning Society (IMLS). Proceedings of Machine Learning Research Vol. 97 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. Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms Vol. PRDA19 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. Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms

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. Lecture Notes in Computer Science Vol. 4698 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. A C M. S I G A C T - S I G M O D - S I G A R T Symposium on Principles of Database Systems. Proceedings 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) [48] Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH. Leibniz International Proceedings in Informatics Vol. 126 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, 18-24 July 2021, Virtual Event (pp. 6011-6020). Proceedings of Machine Learning Research Vol. 139 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. A C M. S I G A C T - S I G M O D - S I G A R T Symposium on Principles of Database Systems. Proceedings No. 2018 https://doi.org/10.1145/3188745.3188790

Larsen, K. G. & Williams, R. (2017). Faster Online Matrix-Vector Multiplication. In P. N. Klein (Ed.), Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms (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 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS) (pp. 61-70). 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 (pp. 486-503). Springer. Lecture Notes in Computer Science Vol. 12550 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. The Annual A C M - S I A M Symposium on Discrete Algorithms. Proceedings 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. Lecture Notes in Computer Science Vol. 8486 https://doi.org/10.1007/978-3-319-07566-2_25

Larsen, K. G. (2011). On Range Searching in the Group Model and Combinatorial Discrepancy. In 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science (FOCS) (pp. 542-549). IEEE Computer Society Press. https://doi.org/10.1109/FOCS.2011.14

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. IEEE International Symposium on Information Theory - Proceedings Vol. 2020-June https://doi.org/10.1109/ISIT44484.2020.9174310

Larsen, K. G. & Nelson, J. (2017). Optimality of the Johnson-Lindenstrauss Lemma. In 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS) (pp. 633-638). [8104096] https://doi.org/10.1109/FOCS.2017.64

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. Lecture Notes in Computer Science Vol. 12238 https://doi.org/10.1007/978-3-030-57990-6_28

Larsen, K. G. (2012). The Cell Probe Complexity of Dynamic Range Counting. In STOC’12 : Proceedings of the 44th symposium on Theory of Computing (pp. 85-94). Association for Computing Machinery. https://doi.org/10.1145/2213977.2213987

Larsen, K. G., Nelson, J. & Nguyen, H. L. (2015). Time Lower Bounds for Nonadaptive Turnstile Streaming Algorithms. In R. Servedio & R. Rubinfeld (Eds.), Proceedings of the Forty-Seventh Annual ACM on Symposium on Theory of Computing, STOC '15 (pp. 803-812). Association for Computing Machinery. https://doi.org/10.1145/2746539.2746542

Larsen, K. G. & Nielsen, J. B. (2018). Yes, There is an Oblivious RAM Lower Bound! In H. Shacham & A. Boldyreva (Eds.), Advances in Cryptology -- CRYPTO 2018 (pp. 523-542). Springer VS. Lecture Notes in Computer Science (LNCS) No. 10992 https://doi.org/10.1007/978-3-319-96881-0_18

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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11926 LNCS https://doi.org/10.1007/978-3-030-39479-0_16

Displaying results 251 to 300 out of 340

Revised 17.04.2023

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