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ORCIDMartin Aumüller 0001
Person information
- affiliation: IT University of Copenhagen, Denmark
- affiliation (PhD 2015): TU Ilmenau, Germany
Other persons with the same name
- Martin Aumüller 0002
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2020 – today
- 2024
[j12]Martin Aumüller, Christian Janos Lebeda, Boel Nelson, Rasmus Pagh:
PLAN: Variance-Aware Private Mean Estimation. Proc. Priv. Enhancing Technol. 2024(3): 606-625 (2024)- 2023
- [j11]Martin Aumüller, Matteo Ceccarello:
Recent Approaches and Trends in Approximate Nearest Neighbor Search, with Remarks on Benchmarking. IEEE Data Eng. Bull. 46(3): 89-105 (2023) - [c22]Omar Shahbaz Khan, Martin Aumüller, Björn Þór Jónsson:
Suitability of Nearest Neighbour Indexes for Multimedia Relevance Feedback. SISAP 2023: 133-147 - [c21]Martin Aumüller, Matteo Ceccarello:
Solving k-Closest Pairs in High-Dimensional Data. SISAP 2023: 200-214 - [c20]Eric Sadit Tellez, Martin Aumüller, Edgar Chávez:
Overview of the SISAP 2023 Indexing Challenge. SISAP 2023: 255-264 - [i20]Martin Aumüller, Christian Janos Lebeda, Boel Nelson, Rasmus Pagh:
PLAN: Variance-Aware Private Mean Estimation. CoRR abs/2306.08745 (2023) - 2022
- [j10]Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri:
Sampling near neighbors in search for fairness. Commun. ACM 65(8): 83-90 (2022) - [j9]Christian Janos Lebeda, Martin Aumüller, Rasmus Pagh:
Representing Sparse Vectors with Differential Privacy, Low Error, Optimal Space, and Fast Access. J. Priv. Confidentiality 12(2) (2022) - [j8]Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri:
Sampling a Near Neighbor in High Dimensions - Who is the Fairest of Them All? ACM Trans. Database Syst. 47(1): 4:1-4:40 (2022) - [c19]Matti Karppa, Martin Aumüller, Rasmus Pagh:
DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search. AISTATS 2022: 3108-3137 - [c18]Martin Aumüller, Matteo Ceccarello:
Implementing Distributed Similarity Joins using Locality Sensitive Hashing. EDBT 2022: 1:78-1:90 - [i19]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:
Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search. CoRR abs/2205.03763 (2022) - 2021
- [j7]Martin Aumüller, Matteo Ceccarello:
The role of local dimensionality measures in benchmarking nearest neighbor search. Inf. Syst. 101: 101807 (2021) - [j6]Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri:
Fair near neighbor search via sampling. SIGMOD Rec. 50(1): 42-49 (2021) - [c17]Martin Aumüller, Christian Janos Lebeda, Rasmus Pagh:
Differentially Private Sparse Vectors with Low Error, Optimal Space, and Fast Access. CCS 2021: 1223-1236 - [c16]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:
Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search. NeurIPS (Competition and Demos) 2021: 177-189 - [i18]Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri:
Sampling a Near Neighbor in High Dimensions - Who is the Fairest of Them All? CoRR abs/2101.10905 (2021) - [i17]Martin Aumüller, Christian Janos Lebeda, Rasmus Pagh:
Differentially private sparse vectors with low error, optimal space, and fast access. CoRR abs/2106.10068 (2021) - [i16]Matti Karppa, Martin Aumüller, Rasmus Pagh:
DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search. CoRR abs/2107.02736 (2021) - 2020
- [j5]Martin Aumüller, Erik Bernhardsson, Alexander John Faithfull:
ANN-Benchmarks: A benchmarking tool for approximate nearest neighbor algorithms. Inf. Syst. 87 (2020) - [c15]Quoc-Tuan Truong, Hady W. Lauw, Martin Aumüller, Naoko Nitta:
Reproducibility Companion Paper: Visual Sentiment Analysis for Review Images with Item-Oriented and User-Oriented CNN. ACM Multimedia 2020: 4444-4447 - [c14]Martin Aumüller, Rasmus Pagh, Francesco Silvestri:
Fair Near Neighbor Search: Independent Range Sampling in High Dimensions. PODS 2020: 191-204 - [c13]Martin Aumüller, Anders Bourgeat, Jana Schmurr:
Differentially Private Sketches for Jaccard Similarity Estimation. SISAP 2020: 18-32 - [c12]Martin Aumüller, Matteo Ceccarello:
Running Experiments with Confidence and Sanity. SISAP 2020: 387-395 - [c11]Martin Aumüller:
Algorithm Engineering for High-Dimensional Similarity Search Problems (Invited Talk). SEA 2020: 1:1-1:3 - [e1]Shin'ichi Satoh, Lucia Vadicamo, Arthur Zimek, Fabio Carrara, Ilaria Bartolini, Martin Aumüller, Björn Þór Jónsson, Rasmus Pagh:
Similarity Search and Applications - 13th International Conference, SISAP 2020, Copenhagen, Denmark, September 30 - October 2, 2020, Proceedings. Lecture Notes in Computer Science 12440, Springer 2020, ISBN 978-3-030-60935-1 [contents] - [i15]Martin Aumüller, Anders Bourgeat, Jana Schmurr:
Differentially Private Sketches for Jaccard Similarity Estimation. CoRR abs/2008.08134 (2020)
2010 – 2019
- 2019
- [j4]Martin Aumüller, Martin Dietzfelbinger, Clemens Heuberger, Daniel Krenn, Helmut Prodinger:
Dual-Pivot Quicksort: Optimality, Analysis and Zeros of Associated Lattice Paths. Comb. Probab. Comput. 28(4): 485-518 (2019) - [c10]Martin Aumüller, Nikolaj Hass:
Simple and Fast BlockQuicksort using Lomuto's Partitioning Scheme. ALENEX 2019: 15-26 - [c9]Martin Aumüller, Tobias Christiani, Rasmus Pagh, Michael Vesterli:
PUFFINN: Parameterless and Universally Fast FInding of Nearest Neighbors. ESA 2019: 10:1-10:16 - [c8]Martin Aumüller, Matteo Ceccarello:
Benchmarking Nearest Neighbor Search: Influence of Local Intrinsic Dimensionality and Result Diversity in Real-World Datasets. EDML@SDM 2019: 14-23 - [c7]Martin Aumüller, Matteo Ceccarello:
The Role of Local Intrinsic Dimensionality in Benchmarking Nearest Neighbor Search. SISAP 2019: 113-127 - [i14]Martin Aumüller, Rasmus Pagh, Francesco Silvestri:
Fair Near Neighbor Search: Independent Range Sampling in High Dimensions. CoRR abs/1906.01859 (2019) - [i13]Martin Aumüller, Tobias Christiani, Rasmus Pagh, Michael Vesterli:
PUFFINN: Parameterless and Universally Fast FInding of Nearest Neighbors. CoRR abs/1906.12211 (2019) - [i12]Martin Aumüller, Matteo Ceccarello:
The Role of Local Intrinsic Dimensionality in Benchmarking Nearest Neighbor Search. CoRR abs/1907.07387 (2019) - 2018
- [c6]Martin Aumüller, Tobias Christiani, Rasmus Pagh, Francesco Silvestri:
Distance-Sensitive Hashing. PODS 2018: 89-104 - [i11]Martin Aumüller, Erik Bernhardsson, Alexander John Faithfull:
ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms. CoRR abs/1807.05614 (2018) - [i10]Martin Aumüller, Nikolaj Hass:
Simple and Fast BlockQuicksort using Lomuto's Partitioning Scheme. CoRR abs/1810.12047 (2018) - 2017
- [c5]Martin Aumüller, Erik Bernhardsson, Alexander John Faithfull:
ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms. SISAP 2017: 34-49 - [c4]Thomas D. Ahle, Martin Aumüller, Rasmus Pagh:
Parameter-free Locality Sensitive Hashing for Spherical Range Reporting. SODA 2017: 239-256 - [i9]Martin Aumüller, Tobias Christiani, Rasmus Pagh, Francesco Silvestri:
Distance-sensitive hashing. CoRR abs/1703.07867 (2017) - [i8]Martin Dietzfelbinger, Michael Mitzenmacher, Rasmus Pagh, David P. Woodruff, Martin Aumüller:
Theory and Applications of Hashing (Dagstuhl Seminar 17181). Dagstuhl Reports 7(5): 1-21 (2017) - 2016
- [j3]Martin Aumüller, Martin Dietzfelbinger:
Optimal Partitioning for Dual-Pivot Quicksort. ACM Trans. Algorithms 12(2): 18:1-18:36 (2016) - [j2]Martin Aumüller, Martin Dietzfelbinger, Pascal Klaue:
How Good Is Multi-Pivot Quicksort? ACM Trans. Algorithms 13(1): 8:1-8:47 (2016) - [i7]Martin Aumüller, Martin Dietzfelbinger, Clemens Heuberger, Daniel Krenn, Helmut Prodinger:
Counting Zeros in Random Walks on the Integers and Analysis of Optimal Dual-Pivot Quicksort. CoRR abs/1602.04031 (2016) - [i6]Thomas D. Ahle, Martin Aumüller, Rasmus Pagh:
High-dimensional Spherical Range Reporting by Output-Sensitive Multi-Probing LSH. CoRR abs/1605.02673 (2016) - [i5]Martin Aumüller, Martin Dietzfelbinger, Philipp Woelfel:
A Simple Hash Class with Strong Randomness Properties in Graphs and Hypergraphs. CoRR abs/1611.00029 (2016) - [i4]Martin Aumüller, Martin Dietzfelbinger, Clemens Heuberger, Daniel Krenn, Helmut Prodinger:
Dual-Pivot Quicksort: Optimality, Analysis and Zeros of Associated Lattice Paths. CoRR abs/1611.00258 (2016) - 2015
- [b1]Martin Aumüller:
On the Analysis of Two Fundamental Randomized Algorithms - Multi-Pivot Quicksort and Efficient Hash Functions. Technische Universität Ilmenau, Germany, 2015 - [i3]Martin Aumüller, Martin Dietzfelbinger, Pascal Klaue:
How Good is Multi-Pivot Quicksort? CoRR abs/1510.04676 (2015) - 2014
- [j1]Martin Aumüller, Martin Dietzfelbinger, Philipp Woelfel:
Explicit and Efficient Hash Families Suffice for Cuckoo Hashing with a Stash. Algorithmica 70(3): 428-456 (2014) - 2013
- [c3]Martin Aumüller, Martin Dietzfelbinger:
Optimal Partitioning for Dual Pivot Quicksort - (Extended Abstract). ICALP (1) 2013: 33-44 - [i2]Martin Aumüller, Martin Dietzfelbinger:
Optimal Partitioning for Dual Pivot Quicksort. CoRR abs/1303.5217 (2013) - 2012
- [c2]Martin Aumüller, Martin Dietzfelbinger, Philipp Woelfel:
Explicit and Efficient Hash Families Suffice for Cuckoo Hashing with a Stash. ESA 2012: 108-120 - [i1]Martin Aumüller, Martin Dietzfelbinger, Philipp Woelfel:
Explicit and Efficient Hash Families Suffice for Cuckoo Hashing with a Stash. CoRR abs/1204.4431 (2012)
2000 – 2009
- 2009
- [c1]Martin Aumüller, Martin Dietzfelbinger, Michael Rink:
Experimental Variations of a Theoretically Good Retrieval Data Structure. ESA 2009: 742-751
Coauthor Index
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last updated on 2024-08-03 20:13 CEST by the dblp team
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