Also see my profiles on DBLP and Google Scholar.

Pre-prints

• 2023: PLAN: Variance-Aware Private Mean Estimation, Martin Aumüller, Christian Janos Lebeda, Boel Nelson, Rasmus Pagh.

Journal articles

• 2022: Representing Sparse Vectors with Differential Privacy, Low Error, Optimal Space, and Fast Access, Martin Aumüller, Christian Janos Lebeda, Rasmus Pagh, Journal of Privacy and Confidentiality (12/02).
• 2022: Sampling near neighbors in search for fairness, Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri. Communications of the ACM. See TODS version below for a full technical version.
• 2022: Sampling a Near Neighbor in High Dimensions — Who is the Fairest of Them All?, Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri. TODS. code
• 2021 Fair near neighbor search via sampling, Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri, ACM SIGMOD Record 50. Technical perspective by Qin Zhang.
• 2021: The Role of Local Dimensionality Measures in Benchmarking Nearest Neighbor Search, Martin Aumüller, Matteo Ceccarello, Information Systems. Available online.
• 2019: ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms, Martin Aumüller, Erik Bernhardsson, Alexander Faithfull. Full version of the SISAP’17 paper below. Information Systems. Appeared online.
• 2018: Dual-Pivot Quicksort: Optimality, Analysis and Zeros of Associated Lattice Paths, Martin Aumüller, Martin Dietzfelbinger, Clemens Heuberger, Daniel Krenn, Helmut Prodinger. Full version of the AofA’16 paper below. Combinatorics, Probability, and Computing. Online version.
• 2016: How Good is Multi-Pivot Quicksort?, Martin Aumüller, Martin Dietzfelbinger, Pascal Klaue. ACM Transactions on Algorithms 13(1). Additional Material
• 2015: Optimal Partitioning for Dual-Pivot Quicksort, Martin Aumüller, Martin Dietzfelbinger, ACM Transactions on Algorithms 12(2).
• 2014: Explicit and Efficient Hash Families Suffice for Cuckoo Hashing with a Stash, Martin Aumüller, Martin Dietzfelbinger, Philipp Woelfel, invited paper, Algorithmica 70(3). Additional Material

Conference papers

• 2023: Solving k-Closest Pairs in High-Dimensional Data, Martin Aumüller, Matteo Ceccarello, SISAP 2023. code
• 2023: Overview of the SISAP 2023 Indexing Challenge, Eric S. Tellez, Martin Aumüller, Edgar Chavez, SISAP 2023. website
• 2023: Suitability of Nearest Neighbour Indexes for Multimedia Relevance Feedback, Omar Shahbaz Khan, Martin Aumüller, Björn Þór Jónsson, SISAP 2023.
• 2022: DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search, Matti Karppa, Martin Aumüller, Rasmus Pagh. AISTATS 2022. code, experiments
• 2022: Implementing Distributed Similarity Joins using Locality Sensitive Hashing, Martin Aumüller, Matteo Ceccarello. EDBT 2022.
• 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, Jingdong Wang. NeurIPS 2021. code
• 2021: Differentially private sparse vectors with low error, optimal space, and fast access, Martin Aumüller, Christian Lebeda, Rasmus Pagh. Accepted at CCS 2021. Also accepted as poster at TPDP 2021.
• 2020: Running experiments with confidence and sanity, Martin Aumüller, Matteo Ceccarello. SISAP 2020. code
• 2020: Locally Differential Private Sketches for Jaccard Similarity Estimation, Martin Aumüller, Anders Bourgeat, Jana Schmurr. SISAP 2020. code
• 2020: Fair Near Neighbor Search: Independent Range Sampling in High Dimensions, Martin Aumüller, Rasmus Pagh, Francesco Silvestri. PODS 2020. code.
• 2019: The Role of Local Intrinsic Dimensionality in Benchmarking Nearest Neighbor Search, Martin Aumüller, Matteo Ceccarello, SISAP 2019 (Best Paper Award). website
• 2019: PUFFINN: Parameterless and Universally Fast FInding of Nearest Neighbors, Martin Aumüller, Tobias Christiani, Rasmus Pagh, Michael Vesterli, ESA 2019. slides, code, additional material
• 2019: Simple and Fast BlockQuicksort using Lomuto’s Partitioning Scheme, Martin Aumüller, Nikolaj Hass. ALENEX’19. code
• 2018: Distance-sensitive Hashing, Martin Aumüller, Tobias Christiani, Rasmus Pagh, Francesco Silvestri. PODS’18, slides
• 2017: ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms, M. Aumüller, E. Bernhardsson, A. Faithfull. SISAP 2017. website
• 2017: Parameter-free Locality Sensitive Hashing for Spherical Range Reporting, Thomas D. Ahle, Martin Aumüller, Rasmus Pagh. SODA 2017, slides
• 2016: Counting Zeros in Random Walks on the Integers and Analysis of Optimal Dual-Pivot Quicksort, Martin Aumüller, Martin Dietzfelbinger, Clemens Heuberger, Daniel Krenn, Helmut Prodinger. AofA 2016
• 2013: Optimal Partitioning for Dual Pivot Quicksort, Martin Aumüller, Martin Dietzfelbinger, ICALP 2013, slides.
• 2012: Explicit and Efficient Hash Families Suffice for Cuckoo Hashing with a Stash, Martin Aumüller, Martin Dietzfelbinger, Philipp Woelfel, ESA 2012, slides.
• 2009: Experimental variations of a theoretically good retrieval data structure, Martin Aumüller, Martin Dietzfelbinger, Michael Rink, ESA 2009.

Other papers

• 2023: Recent Approaches and Trends in Approximate Nearest Neighbor Search, M. Aumüller, M. Ceccarello, IEEE Data Engineering Bulletin.
• 2022: Reproducibility protocol for ANN-Benchmarks: A benchmarking tool for approximate nearest neighbor search algorithms, M. Aumüller, E. Bernhardsson, A. Faithfull, manuscript. Code, Artifacts
• 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, Naoko Nitta, ACM MM 2020.
• 2020: Algorithm Engineering for High-Dimensional Similarity Search Problems (Invited Talk), Martin Aumüller. SEA 2020.
• 2019: Benchmarking Nearest Neighbor Search: Influence of Local Intrinsic Dimensionality and Result Diversity in Real-World Datasets, Martin Aumüller, Matteo Ceccarello. Workshop EDML19. Website
• 2016: A Simple Hash Class with Strong Randomness Properties in Graphs and Hypergraphs, Martin Aumüller, Martin Dietzfelbinger, Philipp Woelfel.

PhD Thesis

• 2015: On the Analysis of Two Fundamental Randomized Algorithms: Multi-Pivot Quicksort and Efficient Hash Functions, Martin Aumüller, TU Ilmenau, slides of defense.

Diploma Thesis

• 2010: An Alternative Analysis of Cuckoo Hashing with a Stash and Realistic Hash Functions, Martin Aumüller, TU Ilmenau.