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Peer-reviewed research papers

  1. DMLR 2026 VIBE: Vector Index Benchmark for Embeddings, Elias Jääsaari, Ville Hyvönen, Matteo Ceccarello, Teemu Roos, and Martin Aumüller. BenchmarkDataWebsiteAlso: VecDB 2026
  2. SIGMOD 2026 Mathematical Foundations of Poisoning Attacks on Linear Regression over Cumulative Distribution Functions, Atsuki Sato, Martin Aumüller, and Yusuke Matsui.
  3. Inf. Syst. 2026 Approximate hierarchical density-based clustering using graph-based search indexes, Camilla Birch Okkels, Erik Thordsen, Martin Aumüller, Arthur Zimek, and Erich Schubert.
  4. EDBT 2025 High-dimensional density-based clustering using locality-sensitive hashing, Camilla Birch Okkels, Martin Aumüller, Viktor Bello Thomsen, and Arthur Zimek. ImplementationBenchmark code
  5. FORC 2025 Differentially Private High-Dimensional Approximate Range Counting, Revisited, Martin Aumüller, Fabrizio Boninsegna, and Francesco Silvestri. Prototype
  6. SISAP 2025 Approximate Single-Linkage Clustering Using Graph-Based Indexes: MST-Based Approaches and Incremental Searchers, Camilla Birch Okkels, Erik Thordsen, Martin Aumüller, Arthur Zimek, and Erich Schubert.
  7. SISAP 2025 Overview of the SISAP 2025 Indexing Challenge, Eric Sadit Tellez, Edgar Chávez, Martin Aumüller, and Vladimir Mic.
  8. NeurIPS 2025 Results of the Big ANN: NeurIPS'23 competition, Harsha Vardhan Simhadri, Martin Aumüller, Amir Ingber, Matthijs Douze, George Williams, Magdalen Dobson Manohar, Dmitry Baranchuk, Edo Liberty, Frank Liu, Benjamin Landrum, Mazin Karjikar, Laxman Dhulipala, Meng Chen, Yue Chen, Rui Ma, Kai Zhang, Yuzheng Cai, Jiayang Shi, Yizhuo Chen, Weiguo Zheng, Zihao Wan, Jie Yin, and Ben Huang. Code
  9. SISAP 2024 An Empirical Evaluation of Search Strategies for Locality-Sensitive Hashing: Lookup, Voting, and Natural Classifier Search, Malte Helin Johnsen, and Martin Aumüller.
  10. SISAP 2024 On the Design of Scalable Outlier Detection Methods Using Approximate Nearest Neighbor Graphs, Camilla Birch Okkels, Martin Aumüller, and Arthur Zimek. Code
  11. SISAP 2024 Overview of the SISAP 2024 Indexing Challenge, Eric Sadit Tellez, Martin Aumüller, and Vladimir Mic.
  12. PoPETs 2024 PLAN: Variance-Aware Private Mean Estimation, Martin Aumüller, Christian Janos Lebeda, Boel Nelson, and Rasmus Pagh.
  13. SISAP 2023 Solving k-Closest Pairs in High-Dimensional Data, Martin Aumüller, and Matteo Ceccarello. Code
  14. SISAP 2023 Suitability of Nearest Neighbour Indexes for Multimedia Relevance Feedback, Omar Shahbaz Khan, Martin Aumüller, and Björn Þór Jónsson.
  15. SISAP 2023 Overview of the SISAP 2023 Indexing Challenge, Eric Sadit Tellez, Martin Aumüller, and Edgar Chávez. Website
  16. Data Eng. Bull. 2023 Recent Approaches and Trends in Approximate Nearest Neighbor Search, with Remarks on Benchmarking, Martin Aumüller, and Matteo Ceccarello.
  17. AISTATS 2022 DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search, Matti Karppa, Martin Aumüller, and Rasmus Pagh. CodeExperiments
  18. EDBT 2022 Implementing Distributed Similarity Joins using Locality Sensitive Hashing, Martin Aumüller, and Matteo Ceccarello.
  19. CACM 2022 Sampling near neighbors in search for fairness, Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, and Francesco Silvestri.
  20. JPC 2022 Representing Sparse Vectors with Differential Privacy, Low Error, Optimal Space, and Fast Access, Christian Janos Lebeda, Martin Aumüller, and Rasmus Pagh.
  21. TODS 2022 Sampling a Near Neighbor in High Dimensions - Who is the Fairest of Them All?, Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, and Francesco Silvestri.
  22. CCS 2021 Differentially Private Sparse Vectors with Low Error, Optimal Space, and Fast Access, Martin Aumüller, Christian Janos Lebeda, and Rasmus Pagh.
  23. NeurIPS 2021 Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search, Harsha Vardhan Simhadri, George Williams, Martin Aumüller, Matthijs Douze, Artem Babenko, Dmitry Baranchuk, Qi Chen, Lucas Hosseini, Ravishankar Krishnaswamy, Gopal Srinivasa, Suhas Jayaram Subramanya, and Jingdong Wang. Code
  24. Inf. Syst. 2021 The role of local dimensionality measures in benchmarking nearest neighbor search, Martin Aumüller, and Matteo Ceccarello.
  25. SIGMOD Record 2021 Fair near neighbor search via sampling, Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, and Francesco Silvestri.
  26. PODS 2020 Fair Near Neighbor Search: Independent Range Sampling in High Dimensions, Martin Aumüller, Rasmus Pagh, and Francesco Silvestri.
  27. SISAP 2020 Differentially Private Sketches for Jaccard Similarity Estimation, Martin Aumüller, Anders Bourgeat, and Jana Schmurr.
  28. SISAP 2020 Running Experiments with Confidence and Sanity, Martin Aumüller, and Matteo Ceccarello. Code
  29. Inf. Syst. 2020 ANN-Benchmarks: A benchmarking tool for approximate nearest neighbor algorithms, Martin Aumüller, Erik Bernhardsson, and Alexander John Faithfull.
  30. ALENEX 2019 Simple and Fast BlockQuicksort using Lomuto's Partitioning Scheme, Martin Aumüller, and Nikolaj Hass.
  31. ESA 2019 PUFFINN: Parameterless and Universally Fast FInding of Nearest Neighbors, Martin Aumüller, Tobias Christiani, Rasmus Pagh, and Michael Vesterli. CodeSlidesMore
  32. EDML 2019 Benchmarking Nearest Neighbor Search: Influence of Local Intrinsic Dimensionality and Result Diversity in Real-World Datasets, Martin Aumüller, and Matteo Ceccarello. Website
  33. SISAP 2019 The Role of Local Intrinsic Dimensionality in Benchmarking Nearest Neighbor Search, Martin Aumüller, and Matteo Ceccarello. Website
  34. CPC 2019 Dual-Pivot Quicksort: Optimality, Analysis and Zeros of Associated Lattice Paths, Martin Aumüller, Martin Dietzfelbinger, Clemens Heuberger, Daniel Krenn, and Helmut Prodinger.
  35. PODS 2018 Distance-Sensitive Hashing, Martin Aumüller, Tobias Christiani, Rasmus Pagh, and Francesco Silvestri. Slides
  36. SISAP 2017 ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms, Martin Aumüller, Erik Bernhardsson, and Alexander John Faithfull. CodeWebsite
  37. SODA 2017 Parameter-free Locality Sensitive Hashing for Spherical Range Reporting, Thomas D. Ahle, Martin Aumüller, and Rasmus Pagh. Slides
  38. TALG 2016 Optimal Partitioning for Dual-Pivot Quicksort, Martin Aumüller, and Martin Dietzfelbinger.
  39. TALG 2016 How Good Is Multi-Pivot Quicksort?, Martin Aumüller, Martin Dietzfelbinger, and Pascal Klaue. More
  40. Algorithmica 2014 Explicit and Efficient Hash Families Suffice for Cuckoo Hashing with a Stash, Martin Aumüller, Martin Dietzfelbinger, and Philipp Woelfel. More
  41. ICALP 2013 Optimal Partitioning for Dual Pivot Quicksort - (Extended Abstract), Martin Aumüller, and Martin Dietzfelbinger. Slides
  42. ESA 2012 Explicit and Efficient Hash Families Suffice for Cuckoo Hashing with a Stash, Martin Aumüller, Martin Dietzfelbinger, and Philipp Woelfel. Slides
  43. ESA 2009 Experimental Variations of a Theoretically Good Retrieval Data Structure, Martin Aumüller, Martin Dietzfelbinger, and Michael Rink.

Other papers

  1. MM 2020 Reproducibility Companion Paper: Visual Sentiment Analysis for Review Images with Item-Oriented and User-Oriented CNN, Quoc-Tuan Truong, Hady W. Lauw, Martin Aumüller, and Naoko Nitta.
  2. SEA 2020 Algorithm Engineering for High-Dimensional Similarity Search Problems (Invited Talk), Martin Aumüller. Slides
  3. Dagstuhl Seminar 17181 Theory and Applications of Hashing (Dagstuhl Seminar 17181), Martin Dietzfelbinger, Michael Mitzenmacher, Rasmus Pagh, David P. Woodruff, and Martin Aumüller.

PhD Thesis

  1. TU Ilmenau 2015 On the Analysis of Two Fundamental Randomized Algorithms - Multi-Pivot Quicksort and Efficient Hash Functions, Martin Aumüller. Slides

Pre-prints

  1. arXiv 2016 A Simple Hash Class with Strong Randomness Properties in Graphs and Hypergraphs, Martin Aumüller, Martin Dietzfelbinger, and Philipp Woelfel.

Co-authors

Thomas D. Ahle (1), Artem Babenko (1), Dmitry Baranchuk (2), Erik Bernhardsson (2), Fabrizio Boninsegna (1), Anders Bourgeat (1), Yuzheng Cai (1), Matteo Ceccarello (8), Meng Chen (1), Yue Chen (1), Yizhuo Chen (1), Qi Chen (1), Tobias Christiani (2), Edgar Chávez (2), Laxman Dhulipala (1), Martin Dietzfelbinger (9), Matthijs Douze (2), Alexander John Faithfull (2), Sariel Har-Peled (3), Nikolaj Hass (1), Clemens Heuberger (1), Lucas Hosseini (1), Ben Huang (1), Ville Hyvönen (1), Amir Ingber (1), Malte Helin Johnsen (1), Elias Jääsaari (1), Björn Þór Jónsson (1), Mazin Karjikar (1), Matti Karppa (1), Omar Shahbaz Khan (1), Pascal Klaue (1), Daniel Krenn (1), Ravishankar Krishnaswamy (1), Benjamin Landrum (1), Hady W. Lauw (1), Christian Janos Lebeda (3), Edo Liberty (1), Frank Liu (1), Rui Ma (1), Sepideh Mahabadi (3), Magdalen Dobson Manohar (1), Yusuke Matsui (1), Vladimir Mic (2), Michael Mitzenmacher (1), Boel Nelson (1), Naoko Nitta (1), Camilla Birch Okkels (4), Rasmus Pagh (12), Helmut Prodinger (1), Michael Rink (1), Teemu Roos (1), Atsuki Sato (1), Jana Schmurr (1), Erich Schubert (2), Jiayang Shi (1), Francesco Silvestri (6), Harsha Vardhan Simhadri (2), Gopal Srinivasa (1), Suhas Jayaram Subramanya (1), Eric Sadit Tellez (3), Viktor Bello Thomsen (1), Erik Thordsen (2), Quoc-Tuan Truong (1), Michael Vesterli (1), Zihao Wan (1), Jingdong Wang (1), George Williams (2), Philipp Woelfel (3), David P. Woodruff (1), Jie Yin (1), Kai Zhang (1), Weiguo Zheng (1), Arthur Zimek (4)