Also see my profiles on DBLP and Google Scholar.

Submitted papers

• 2021: 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. Submitted to a journal. code
• 2020: The Role of Local Dimensionality Measures in Benchmarking Nearest Neighbor Search, Martin Aumüller, Matteo Ceccarello, Submitted to a journal.
• 2016: A Simple Hash Class with Strong Randomness Properties in Graphs and Hypergraphs, Martin Aumüller, Martin Dietzfelbinger, Philipp Woelfel. Submitted to a journal.

Journal articles

• 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

• 2020: Running experiments with confidence and sanity, Martin Aumüller, Matteo Ceccarello. Accepted at SISAP 2020. code
• 2020: Locally Differential Private Sketches for Jaccard Similarity Estimation, Martin Aumüller, Anders Bourgeat, Jana Schmurr. Accepted at 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

• 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

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.