| 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 | 143 | 2017 |
| Optimal partitioning for dual-pivot quicksort M Aumüller, M Dietzfelbinger ACM Transactions on Algorithms (TALG) 12 (2), 1-36, 2015 | 43 | 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 | 37 | 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 | 35 | 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 | 21 | 2018 |
| How good is multi-pivot quicksort? M Aumüller, M Dietzfelbinger, P Klaue ACM Transactions on Algorithms (TALG) 13 (1), 1-47, 2016 | 19 | 2016 |
| Experimental variations of a theoretically good retrieval data structure M Aumüller, M Dietzfelbinger, M Rink European Symposium on Algorithms, 742-751, 2009 | 12 | 2009 |
| PUFFINN: parameterless and universally fast finding of nearest neighbors M Aumüller, T Christiani, R Pagh, M Vesterli arXiv preprint arXiv:1906.12211, 2019 | 10 | 2019 |
| 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 | 9* | 2019 |
| The role of local dimensionality measures in benchmarking nearest neighbor search M Aumüller, M Ceccarello Information Systems, 101807, 2021 | 8* | 2021 |
| 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 | 7* | 2021 |
| Differentially Private Sketches for Jaccard Similarity Estimation M Aumüller, A Bourgeat, J Schmurr International Conference on Similarity Search and Applications, 18-32, 2020 | 4 | 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 | 4 | 2016 |
| An alternative analysis of cuckoo hashing with a stash and realistic hash functions M Aumüller Technische Universität Ilmenau, 2010 | 4 | 2010 |
| On the Analysis of Two Fundamental Randomized Algorithms-Multi-Pivot Quicksort and Efficient Hash Functions M Aumüller | 3* | 2015 |
| 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 | 1 | 2021 |
| Reproducibility Companion Paper: Visual Sentiment Analysis for Review Images with Item-Oriented and User-Oriented CNN QT Truong, HW Lauw, M Aumüller, N Nitta Proceedings of the 28th ACM International Conference on Multimedia, 4444-4447, 2020 | 1 | 2020 |
| Algorithm Engineering for High-Dimensional Similarity Search Problems (Invited Talk) M Aumüller 18th International Symposium on Experimental Algorithms (SEA 2020), 2020 | 1 | 2020 |
| 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 | 1 | 2019 |
| DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search M Karppa, M Aumüller, R Pagh arXiv preprint arXiv:2107.02736, 2021 | | 2021 |
Martin DietzfelbingerProfessor of Computer Science, Technische Universität IlmenauVerified email at tu-ilmenau.de
