Experimental Variations of a Theoretically Good Retrieval Data Structure. - Martin Aumüller 001; Martin Dietzfelbinger; Michael Rink 001 (ESA, 2009)

Optimal Partitioning for Dual Pivot Quicksort - (Extended Abstract). - Martin Aumüller 001; Martin Dietzfelbinger (ICALP (1), 2013)

Explicit and Efficient Hash Families Suffice for Cuckoo Hashing with a Stash. - Martin Aumüller 001; Martin Dietzfelbinger; Philipp Woelfel (Algorithmica, 2014)

On the Analysis of Two Fundamental Randomized Algorithms - Multi-Pivot Quicksort and Efficient Hash Functions. - Martin Aumüller 001 (2015)

High-dimensional Spherical Range Reporting by Output-Sensitive Multi-Probing LSH. - Thomas D. Ahle; Martin Aumüller 001; Rasmus Pagh (CoRR, 2016)

Counting Zeros in Random Walks on the Integers and Analysis of Optimal Dual-Pivot Quicksort. - Martin Aumüller 001; Martin Dietzfelbinger; Clemens Heuberger; Daniel Krenn; Helmut Prodinger (CoRR, 2016)

A Simple Hash Class with Strong Randomness Properties in Graphs and Hypergraphs. - Martin Aumüller 001; Martin Dietzfelbinger; Philipp Woelfel (CoRR, 2016)

Optimal Partitioning for Dual-Pivot Quicksort. - Martin Aumüller 001; Martin Dietzfelbinger (ACM Trans. Algorithms, 2016)

How Good Is Multi-Pivot Quicksort? - Martin Aumüller 001; Martin Dietzfelbinger; Pascal Klaue (ACM Trans. Algorithms, 2016)

Parameter-free Locality Sensitive Hashing for Spherical Range Reporting. - Thomas D. Ahle; Martin Aumüller 001; Rasmus Pagh (SODA, 2017)

Theory and Applications of Hashing (Dagstuhl Seminar 17181). - Martin Dietzfelbinger; Michael Mitzenmacher; Rasmus Pagh; David P. Woodruff; Martin Aumüller 001 (Dagstuhl Reports, 2017)

Distance-Sensitive Hashing. - Martin Aumüller 001; Tobias Christiani; Rasmus Pagh; Francesco Silvestri 001 (PODS, 2018)

Simple and Fast BlockQuicksort using Lomuto's Partitioning Scheme. - Martin Aumüller 001; Nikolaj Hass (ALENEX, 2019)

PUFFINN: Parameterless and Universally Fast FInding of Nearest Neighbors. - Martin Aumüller 001; Tobias Christiani; Rasmus Pagh; Michael Vesterli (ESA, 2019)

Benchmarking Nearest Neighbor Search: Influence of Local Intrinsic Dimensionality and Result Diversity in Real-World Datasets. - Martin Aumüller 001; Matteo Ceccarello (EDML@SDM, 2019)

The Role of Local Intrinsic Dimensionality in Benchmarking Nearest Neighbor Search. - Martin Aumüller 001; Matteo Ceccarello (SISAP, 2019)

Dual-Pivot Quicksort: Optimality, Analysis and Zeros of Associated Lattice Paths. - Martin Aumüller 001; Martin Dietzfelbinger; Clemens Heuberger; Daniel Krenn; Helmut Prodinger (Comb. Probab. Comput., 2019)

Reproducibility Companion Paper: Visual Sentiment Analysis for Review Images with Item-Oriented and User-Oriented CNN. - Quoc-Tuan Truong; Hady W. Lauw; Martin Aumüller 001; Naoko Nitta (ACM Multimedia, 2020)

Fair Near Neighbor Search: Independent Range Sampling in High Dimensions. - Martin Aumüller 001; Rasmus Pagh; Francesco Silvestri 001 (PODS, 2020)

Differentially Private Sketches for Jaccard Similarity Estimation. - Martin Aumüller 001; Anders Bourgeat; Jana Schmurr (SISAP, 2020)

Running Experiments with Confidence and Sanity. - Martin Aumüller 001; Matteo Ceccarello (SISAP, 2020)

Algorithm Engineering for High-Dimensional Similarity Search Problems (Invited Talk). - Martin Aumüller 001 (SEA, 2020)

ANN-Benchmarks: A benchmarking tool for approximate nearest neighbor algorithms. - Martin Aumüller 001; Erik Bernhardsson; Alexander John Faithfull (Inf. Syst., 2020)

Differentially Private Sparse Vectors with Low Error, Optimal Space, and Fast Access. - Martin Aumüller 001; Christian Janos Lebeda; Rasmus Pagh (CCS, 2021)

Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search. - Harsha Vardhan Simhadri; George Williams; Martin Aumüller 001; Matthijs Douze; Artem Babenko; Dmitry Baranchuk; Qi Chen 009; Lucas Hosseini; Ravishankar Krishnaswamy; Gopal Srinivasa; Suhas Jayaram Subramanya; Jingdong Wang 001 (NeurIPS (Competition and Demos), 2021)

The role of local dimensionality measures in benchmarking nearest neighbor search. - Martin Aumüller 001; Matteo Ceccarello (Inf. Syst., 2021)

Fair near neighbor search via sampling. - Martin Aumüller 001; Sariel Har-Peled; Sepideh Mahabadi; Rasmus Pagh; Francesco Silvestri 001 (SIGMOD Rec., 2021)

DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search. - Matti Karppa; Martin Aumüller 001; Rasmus Pagh (AISTATS, 2022)

Implementing Distributed Similarity Joins using Locality Sensitive Hashing. - Martin Aumüller 001; Matteo Ceccarello (EDBT, 2022)

Sampling near neighbors in search for fairness. - Martin Aumüller 001; Sariel Har-Peled; Sepideh Mahabadi; Rasmus Pagh; Francesco Silvestri 001 (Commun. ACM, 2022)

Representing Sparse Vectors with Differential Privacy, Low Error, Optimal Space, and Fast Access. - Christian Janos Lebeda; Martin Aumüller 001; Rasmus Pagh (J. Priv. Confidentiality, 2022)

Sampling a Near Neighbor in High Dimensions - Who is the Fairest of Them All? - Martin Aumüller 001; Sariel Har-Peled; Sepideh Mahabadi; Rasmus Pagh; Francesco Silvestri 001 (ACM Trans. Database Syst., 2022)

Solving k-Closest Pairs in High-Dimensional Data. - Martin Aumüller 001; Matteo Ceccarello (SISAP, 2023)

Suitability of Nearest Neighbour Indexes for Multimedia Relevance Feedback. - Omar Shahbaz Khan; Martin Aumüller 001; Björn Þór Jónsson 001 (SISAP, 2023)

Overview of the SISAP 2023 Indexing Challenge. - Eric Sadit Tellez; Martin Aumüller 001; Edgar Chávez (SISAP, 2023)

Recent Approaches and Trends in Approximate Nearest Neighbor Search, with Remarks on Benchmarking. - Martin Aumüller 001; Matteo Ceccarello (IEEE Data Eng. Bull., 2023)

An Empirical Evaluation of Search Strategies for Locality-Sensitive Hashing: Lookup, Voting, and Natural Classifier Search. - Malte Helin Johnsen; Martin Aumüller 001 (SISAP, 2024)

On the Design of Scalable Outlier Detection Methods Using Approximate Nearest Neighbor Graphs. - Camilla Birch Okkels; Martin Aumüller 001; Arthur Zimek (SISAP, 2024)

Overview of the SISAP 2024 Indexing Challenge. - Eric Sadit Tellez; Martin Aumüller 001; Vladimir Mic (SISAP, 2024)

A Simple Linear Space Data Structure for ANN with Application in Differential Privacy. - Martin Aumüller 001; Fabrizio Boninsegna; Francesco Silvestri 001 (CoRR, 2024)

Results of the Big ANN: NeurIPS'23 competition. - Harsha Vardhan Simhadri; Martin Aumüller 001; Amir Ingber; Matthijs Douze; George Williams; Magdalen Dobson Manohar; Dmitry Baranchuk; Edo Liberty; Frank Liu 007; Benjamin Landrum; Mazin Karjikar; Laxman Dhulipala; et al. (CoRR, 2024)

PLAN: Variance-Aware Private Mean Estimation. - Martin Aumüller 001; Christian Janos Lebeda; Boel Nelson; Rasmus Pagh (Proc. Priv. Enhancing Technol., 2024)

High-dimensional density-based clustering using locality-sensitive hashing. - Camilla Birch Okkels; Martin Aumüller 001; Viktor Bello Thomsen; Arthur Zimek (EDBT, 2025)

Differentially Private High-Dimensional Approximate Range Counting, Revisited. - Martin Aumüller 001; Fabrizio Boninsegna; Francesco Silvestri 001 (FORC, 2025)

VIBE: Vector Index Benchmark for Embeddings. - Elias Jääsaari; Ville Hyvönen; Matteo Ceccarello; Teemu Roos; Martin Aumüller 001 (CoRR, 2025)