| ANN-Benchmarks: A benchmarking tool for approximate nearest neighbor algorithms M Aumüller, E Bernhardsson, A Faithfull International Conference on Similarity Search and Applications, 34-49, 2017 | 228 | 2017 |
| Optimal partitioning for dual-pivot quicksort M Aumüller, M Dietzfelbinger ACM Transactions on Algorithms (TALG) 12 (2), 1-36, 2015 | 45 | 2015 |
| Explicit and efficient hash families suffice for cuckoo hashing with a stash M Aumüller, M Dietzfelbinger, P Woelfel Algorithmica 70 (3), 428-456, 2014 | 42 | 2014 |
| Parameter-free locality sensitive hashing for spherical range reporting TD Ahle, M Aumüller, R Pagh Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete …, 2017 | 39 | 2017 |
| Distance-sensitive hashing M Aumüller, T Christiani, R Pagh, F Silvestri Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of …, 2018 | 24 | 2018 |
| How good is multi-pivot quicksort? M Aumüller, M Dietzfelbinger, P Klaue ACM Transactions on Algorithms (TALG) 13 (1), 1-47, 2016 | 24 | 2016 |
| PUFFINN: parameterless and universally fast finding of nearest neighbors M Aumüller, T Christiani, R Pagh, M Vesterli arXiv preprint arXiv:1906.12211, 2019 | 18 | 2019 |
| The role of local dimensionality measures in benchmarking nearest neighbor search M Aumüller, M Ceccarello Information Systems 101, 101807, 2021 | 15* | 2021 |
| Experimental variations of a theoretically good retrieval data structure M Aumüller, M Dietzfelbinger, M Rink Algorithms-ESA 2009: 17th Annual European Symposium, Copenhagen, Denmark …, 2009 | 14 | 2009 |
| Sampling a Near Neighbor in High Dimensions--Who is the Fairest of Them All? M Aumüller, S Har-Peled, S Mahabadi, R Pagh, F Silvestri arXiv preprint arXiv:2101.10905, 2021 | 11* | 2021 |
| Dual-pivot quicksort: Optimality, analysis and zeros of associated lattice paths M Aumüller, M Dietzfelbinger, C Heuberger, D Krenn, H Prodinger Combinatorics, Probability and Computing 28 (4), 485-518, 2019 | 10* | 2019 |
| Fair near neighbor search via sampling M Aumuller, S Har-Peled, S Mahabadi, R Pagh, F Silvestri ACM SIGMOD Record 50 (1), 42-49, 2021 | 7 | 2021 |
| Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search H Vardhan Simhadri, G Williams, M Aumüller, M Douze, A Babenko, ... arXiv e-prints, arXiv: 2205.03763, 2022 | 6* | 2022 |
| An alternative analysis of cuckoo hashing with a stash and realistic hash functions M Aumüller Technische Universität Ilmenau, 2010 | 6 | 2010 |
| Differentially private sketches for Jaccard similarity estimation M Aumüller, A Bourgeat, J Schmurr Similarity Search and Applications: 13th International Conference, SISAP …, 2020 | 5 | 2020 |
| A simple hash class with strong randomness properties in graphs and hypergraphs M Aumüller, M Dietzfelbinger, P Woelfel arXiv preprint arXiv:1611.00029, 2016 | 5 | 2016 |
| Representing Sparse Vectors with Differential Privacy, Low Error, Optimal Space, and Fast Access CJ Lebeda, M Aumüller, R Pagh Journal of Privacy and Confidentiality 12 (2), 2022 | 3* | 2022 |
| DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search M Karppa, M Aumüller, R Pagh International Conference on Artificial Intelligence and Statistics, 3108-3137, 2022 | 3 | 2022 |
| On the Analysis of Two Fundamental Randomized Algorithms-Multi-Pivot Quicksort and Efficient Hash Functions M Aumüller | 3* | 2015 |
| Simple and Fast BlockQuicksort using Lomuto's Partitioning Scheme M Aumüller, N Hass 2019 Proceedings of the Twenty-First Workshop on Algorithm Engineering and …, 2019 | 2 | 2019 |
Rasmus PaghProfessor of computer science, University of CopenhagenVerified email at di.ku.dk
