Search Results - "Aumuller, Martin"

ANN-Benchmarks: A benchmarking tool for approximate nearest neighbor algorithms by Aumüller, Martin, Bernhardsson, Erik, Faithfull, Alexander

“…This paper describes ANN-Benchmarks, a tool for evaluating the performance of in-memory approximate nearest neighbor algorithms. It provides a standard…”
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The role of local dimensionality measures in benchmarking nearest neighbor search by Aumüller, Martin, Ceccarello, Matteo

“…This paper reconsiders common benchmarking approaches to nearest neighbor search. It is shown that the concepts of local intrinsic dimensionality (LID), local…”
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Representing Sparse Vectors with Differential Privacy, Low Error, Optimal Space, and Fast Access by Aumüller, Martin, Lebeda, Christian Janos, Pagh, Rasmus

“…Representing a sparse histogram, or more generally a sparse vector, is a fundamental task in differential privacy. An ideal solution would use space close to…”
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A Global Grid for Analysis of Arthropod Evolution by Stewart, Craig A., Keller, Rainer, Repasky, Richard, Hess, Matthias, Hart, David, Muller, Matthias, Sheppard, Ray, Wossner, Uwe, Aumuller, Martin, Li, Huian, Berry, Donald K., Colbourne, John

“…Maximum likelihood analysis is a powerful technique for inferring evolutionary histories from genetic sequence data. During the fall of 2003, an international…”
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Explicit and Efficient Hash Families Suffice for Cuckoo Hashing with a Stash by Aumüller, Martin, Dietzfelbinger, Martin, Woelfel, Philipp

“…It is shown that for cuckoo hashing with a stash as proposed by Kirsch et al. (Proc. 16th European Symposium on Algorithms (ESA), pp. 611–622, Springer,…”
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Explicit and Efficient Hash Families Suffice for Cuckoo Hashing with a Stash: Algorithm Design and Analysis by AUMÜLLER, Martin, DIETZFELBINGER, Martin, WOELFEL, Philipp

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A Simple Linear Space Data Structure for ANN with Application in Differential Privacy by Aumüller, Martin, Boninsegna, Fabrizio, Silvestri, Francesco

“…Locality Sensitive Filters are known for offering a quasi-linear space data structure with rigorous guarantees for the Approximate Near Neighbor search…”
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The Role of Local Intrinsic Dimensionality in Benchmarking Nearest Neighbor Search by Aumüller, Martin, Ceccarello, Matteo

“…This paper reconsiders common benchmarking approaches to nearest neighbor search. It is shown that the concept of local intrinsic dimensionality (LID) allows…”
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Simple and Fast BlockQuicksort using Lomuto's Partitioning Scheme by Aumüller, Martin, Hass, Nikolaj

“…This paper presents simple variants of the BlockQuicksort algorithm described by Edelkamp and Weiss (ESA 2016). The simplification is achieved by using…”
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PLAN: Variance-Aware Private Mean Estimation by Aumüller, Martin, Lebeda, Christian Janos, Nelson, Boel, Pagh, Rasmus

“…Differentially private mean estimation is an important building block in privacy-preserving algorithms for data analysis and machine learning. Though the…”
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DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search by Karppa, Matti, Aumüller, Martin, Pagh, Rasmus

“…Kernel Density Estimation (KDE) is a nonparametric method for estimating the shape of a density function, given a set of samples from the distribution…”
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Differentially Private Sparse Vectors with Low Error, Optimal Space, and Fast Access by Aumüller, Martin, Lebeda, Christian Janos, Pagh, Rasmus

“…Representing a sparse histogram, or more generally a sparse vector, is a fundamental task in differential privacy. An ideal solution would use space close to…”
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Differentially Private Sketches for Jaccard Similarity Estimation by Aumüller, Martin, Bourgeat, Anders, Schmurr, Jana

“…This paper describes two locally-differential private algorithms for releasing user vectors such that the Jaccard similarity between these vectors can be…”
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Fair Near Neighbor Search: Independent Range Sampling in High Dimensions by Aumüller, Martin, Pagh, Rasmus, Silvestri, Francesco

“…Similarity search is a fundamental algorithmic primitive, widely used in many computer science disciplines. There are several variants of the similarity search…”
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ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms by Aumüller, Martin, Bernhardsson, Erik, Faithfull, Alexander

“…This paper describes ANN-Benchmarks, a tool for evaluating the performance of in-memory approximate nearest neighbor algorithms. It provides a standard…”
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PUFFINN: Parameterless and Universally Fast FInding of Nearest Neighbors by Aumüller, Martin, Christiani, Tobias, Pagh, Rasmus, Vesterli, Michael

“…We present PUFFINN, a parameterless LSH-based index for solving the $k$-nearest neighbor problem with probabilistic guarantees. By parameterless we mean that…”
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Results of the Big ANN: NeurIPS'23 competition by Simhadri, Harsha Vardhan, Aumüller, Martin, Ingber, Amir, Douze, Matthijs, Williams, George, Manohar, Magdalen Dobson, Baranchuk, Dmitry, Liberty, Edo, Liu, Frank, Landrum, Ben, Karjikar, Mazin, Dhulipala, Laxman, Chen, Meng, Chen, Yue, Ma, Rui, Zhang, Kai, Cai, Yuzheng, Shi, Jiayang, Chen, Yizhuo, Zheng, Weiguo, Wan, Zihao, Yin, Jie, Huang, Ben

“…The 2023 Big ANN Challenge, held at NeurIPS 2023, focused on advancing the state-of-the-art in indexing data structures and search algorithms for practical…”
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Sampling a Near Neighbor in High Dimensions -- Who is the Fairest of Them All? by Aumüller, Martin, Har-Peled, Sariel, Mahabadi, Sepideh, Pagh, Rasmus, Silvestri, Francesco

“…Similarity search is a fundamental algorithmic primitive, widely used in many computer science disciplines. Given a set of points $S$ and a radius parameter…”
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Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search by Simhadri, Harsha Vardhan, Williams, George, Aumüller, Martin, Douze, Matthijs, Babenko, Artem, Baranchuk, Dmitry, Chen, Qi, Hosseini, Lucas, Krishnaswamy, Ravishankar, Srinivasa, Gopal, Subramanya, Suhas Jayaram, Wang, Jingdong

“…Despite the broad range of algorithms for Approximate Nearest Neighbor Search, most empirical evaluations of algorithms have focused on smaller datasets,…”
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A Simple Hash Class with Strong Randomness Properties in Graphs and Hypergraphs by Aumüller, Martin, Dietzfelbinger, Martin, Woelfel, Philipp

“…We study randomness properties of graphs and hypergraphs generated by simple hash functions. Several hashing applications can be analyzed by studying the…”
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