Publikationer

Søg publikationer fra Institut for Datalogi

Liste over publikationer af videnskabelige medarbejdere fra Institut for Datalogi, Aarhus Universitet.

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Laursen, M. R., Xu, W. & Møller, A. (2024). Reducing Static Analysis Unsoundness with Approximate Interpretation. Proceedings of the ACM on Programming Languages , 8, 1165-1188. https://doi.org/10.1145/3656424

Larsen-Ledet, I. (2018). Privacy in Collaborative Work: A Situated Challenge. 1. Abstract fra 2018 CHI Conference on Human Factors in Computing Systems, CHI 2018, Montreal, Canada.

Larsen-Ledet, I., Bressa, N. A. & Vermeulen, J. (2019). Reflections on Teaching a Mandatory HCI Course to Computer Science Undergraduates. Afhandling præsenteret på The 2019 EduCHI Symposium on HCI Teaching and Learning, Glasgow, Storbritannien. https://educhi2019.hcilivingcurriculum.org/wp-content/uploads/2019/04/p09-Reflections-on-Teaching-a-Mandatory-HCI-Course-to-Computer-Science-Undergraduates.pdf

Larsen-Ledet, I. (2020). Understanding and Supporting Collaborative Academic Writing as a Fragmented Process. Afhandling præsenteret på The ECSCW 2019 Doctoral Colloquium, Salzburg, Østrig. https://doi.org/10.18420/ecscw2019_dc6

Larsen-Ledet, I. & Korsgaard, H. (2019). Territorial Functioning in Collaborative Writing: Fragmented Exchanges and Common Outcomes. Computer Supported Cooperative Work, 28(3-4), 391-433. https://doi.org/10.1007/s10606-019-09359-8

Larsen-Ledet, I., Korsgaard, H. & Bødker, S. (2020). Collaborative Writing Across Multiple Artifact Ecologies. I CHI 2020 - Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems Artikel 3376422 Association for Computing Machinery. https://doi.org/10.1145/3313831.3376422

Larsen-Ledet, I. (2020). More Than Writing Text: Multiplicity in Collaborative Academic Writing. Århus Universitet.

Larsen-Ledet, I. & Borowski, M. (2020). "It Looks Like You Don't Agree": Idiosyncratic Needs and Practices in Collaborative Writing. I N. Ahmadpour, T. Leong, B. Ploderer, C. Parker, S. Webber, D. Munoz, L. Loke & M. Tomitsch (red.), OzCHI '20: 32nd Australian Conference on Human-Computer Interaction (s. 339-354). Association for Computing Machinery. https://doi.org/10.1145/3441000.3441032

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

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

Larsen, K. G. & Pagh, R. (2012). I/O-Efficient Data Structures for Colored Range and Prefix Reporting. The Annual A C M - S I A M Symposium on Discrete Algorithms. Proceedings, 23, 583-592. http://siam.omnibooksonline.com/2012SODA/index.html

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

Larsen, K. G. (2013). Models and Techniques for Proving Data Structure Lower Bounds. Department of Computer Science, Aarhus University.

Larsen, K. G. & Walderveen, F. V. (2013). Near-Optimal Range Reporting Structures for Categorical. I Proceedings of the 24th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2013 (s. 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. I A. Kulikov, S. O. Kuznetsov & P. Pevzner (red.), 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 (s. 242-251 ). Springer VS. https://doi.org/10.1007/978-3-319-07566-2_25

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

Larsen, K. G., Munro, J. I., Nielsen, J. S. & Thankachan, S. V. (2015). On hardness of several string indexing problems. Theoretical Computer Science, 582, 74-82. https://doi.org/10.1016/j.tcs.2015.03.026

Larsen, K. G. & Williams, R. (2017). Faster Online Matrix-Vector Multiplication. I P. N. Klein (red.), 28th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2017 (Bind PRDA17, s. 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. I Proceedings - 57th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2016 (s. 61-70). Artikel 7782918 IEEE. https://doi.org/10.1109/FOCS.2016.16

Larsen, K. G. & Nelson, J. (2016). The Johnson-Lindenstrauss Lemma Is Optimal for Linear Dimensionality Reduction. Leibniz International Proceedings in Informatics, 55, 82:1 - 82:11. https://doi.org/10.4230/LIPIcs.ICALP.2016.82

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

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

Larsen, K. G. & Nielsen, J. B. (2018). Yes, There is an Oblivious RAM Lower Bound! I H. Shacham & A. Boldyreva (red.), Advances in Cryptology -- CRYPTO 2018 (s. 523-542). Springer VS. https://doi.org/10.1007/978-3-319-96881-0_18

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

Larsen, K. G. (2014). ON range searching in the group model and combinatorial discrepancy. S I A M Journal on Computing, 43(2), 673-686. https://doi.org/10.1137/120865240

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

Larsen, K. G. (2019). Constructive Discrepancy Minimization with Hereditary L2 Guarantees. I R. Niedermeier & C. Paul (red.), 36th International Symposium on Theoretical Aspects of Computer Science (STACS 2019) Artikel 48 Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.STACS.2019.48

Larsen, M. R. E., Han, W. & Schulz, H.-J. (2020). Sketchy Rendering to Aid the Recollection of Regular Visualizations. I EuroVis 2020: Short Papers (s. 139-143). Eurographics Association. https://doi.org/10.2312/evs.20201062

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

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

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

Larsen, K. G., Starup, J. L. & Steensgaard, J. (2021). Further Unifying the Landscape of Cell Probe Lower Bounds. Afhandling præsenteret på Happening virtually , -. https://cs.au.dk/~larsen/papers/FurtherUnifying.pdf

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

Larsen, K. G. & Ritzert, M. (2022). Optimal Weak to Strong Learning. arxiv.org. https://doi.org/10.48550/arXiv.2206.01563

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

Larsen, K. G. (2023). Bagging is an Optimal PAC Learner. I G. Neu & L. Rosasco (red.), Proceedings of COLT 2023 (s. 450-468). MLResearch Press.

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

Larsen, C. A., Schmidt, S. M., Steensgaard, J., Jakobsen, A. B., Pol, J. V. D. & Pavlogiannis, A. (2023). A Truly Symbolic Linear-Time Algorithm for SCC Decomposition. I S. Sankaranarayanan & N. Sharygina (red.), Tools and Algorithms for the Construction and Analysis of Systems: 29th International Conference, TACAS 2023, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2023, Paris, France, April 22–27, 2023, Proceedings, Part II (s. 353-371). Springer. https://doi.org/10.1007/978-3-031-30820-8_22

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

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

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

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

Laoudias, C., Nikitin, A., Karras, P., Youssef, M. & Zeinalipour-Yazti, D. (2021). Indoor Quality-of-Position Visual Assessment Using Crowdsourced Fingerprint Maps. ACM Transactions on Spatial Algorithms and Systems, 7(2), Artikel 10. https://doi.org/10.1145/3433026

Lanng, H., Lykkegaard, A., Thornholm, S., Tzannis, P. & Verdezoto, N. (2016). PeaceKeeper: Augmenting the Awareness and Communication of Noise Pollution in Student Dormitories. I Proceedings of the 7th Augmented Human International Conference, AH 2016: AH '16 Artikel 39 Association for Computing Machinery. https://doi.org/10.1145/2875194.2875218

Landis, D. & Schwartzbach, N. (2023). Stackelberg Attacks on Auctions and Blockchain Transaction Fee Mechanisms. I K. Gal, K. Gal, A. Nowe, G. J. Nalepa, R. Fairstein & R. Radulescu (red.), ECAI 2023 - 26th European Conference on Artificial Intelligence, including 12th Conference on Prestigious Applications of Intelligent Systems, PAIS 2023 - Proceedings (s. 2073-2080). IOS Press. https://doi.org/10.3233/FAIA230501

Lampropoulos, K., Zardbani, F., Mamoulis, N. & Karras, P. (2023). Adaptive Indexing in High-Dimensional Metric Spaces. Proceedings of the VLDB Endowment, 16(10), 2525-2537. https://doi.org/10.14778/3603581.3603592

Lambertini, M., Magnani, M., Marzolla, M., Montesi, D. & Paolino, C. (2014). Large-Scale Social Network Analysis. I A. Gkoulalas-Divanis & A. Labbi (red.), Large scale data analytics (s. 155-187). Springer. https://doi.org/10.1007/978-1-4614-9242-9

Lam, T.-W., Lee, L. K., To, I. K. K. & Wong, P. W. H. (2013). Online Speed Scaling Based on Active Job Count to Minimize Flow Plus Energy. Algorithmica, 65(3), 605-633 . https://doi.org/10.1007/s00453-012-9613-y

Lacerda, F. G., Renes, J. M. & Scholz, V. B. (2016). Coherent state constellations for Bosonic Gaussian channels. I Proceedings - ISIT 2016; 2016 IEEE International Symposium on Information Theory (s. 2499-2503). Artikel 7541749 IEEE. https://doi.org/10.1109/ISIT.2016.7541749

Lacerda, F., Renes, J. M. & Scholz, V. B. (2017). Coherent-state constellations and polar codes for thermal Gaussian channels. Physical Review A, 95(6), Artikel 062343. https://doi.org/10.1103/PhysRevA.95.062343

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