Martin Aumüller

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Dyk ned i forskningsemnerne, hvor Martin Aumüller er aktive. Disse emneetiketter kommer fra dennes persons arbejder. Sammen danner de et unikt fingerprint.

1 Lignende profiler

17 Konferencebidrag i proceedings
8 Tidsskriftartikel

DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search
Aumüller, M., Karppa, M. & Pagh, R., 2022, AISTATS 2022: International Conference on Artificial Intelligence and Statistics. Bind 151. s. 3108-3137
Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › peer review

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Implementing Distributed Approximate Similarity Joins using Locality Sensitive Hashing
Aumüller, M. & Ceccarello, M., 2022, EDBT: International Conference on Extending Database Technology. 13 s.
Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › peer review

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Representing Sparse Vectors with Differential Privacy, Low Error, Optimal Space, and Fast Access
Aumüller, M., Lebeda, C. J. & Pagh, R., 2022, I: Journal of Privacy and Confidentiality. 12, 2
Publikation: Artikel i tidsskrift og konference artikel i tidsskrift › Tidsskriftartikel › Forskning › peer review

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Results of the NeurIPS’21 Challenge on Billion-Scale Approximate Nearest Neighbor Search
Aumüller, M., Simhadri, H., Williams, G., Douze, M., Babenko, A., Baranchuk, D., Hosseini, L., Krishnaswamny, R., Srinivasa, G., Subramanya, S. J. & Wang, J., 2022, Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track. Bind 176. s. 177-189
Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › peer review

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Sampling a Near Neighbor in High Dimensions — Who is the Fairest of Them All?
Aumüller, M., Pagh, R., Silvestri, F., Har-Peled, S. & Mahabadi, S., 2022, I: ACM Transactions on Database Systems. 47, 1, s. 1-40 40 s., 4.
Publikation: Artikel i tidsskrift og konference artikel i tidsskrift › Tidsskriftartikel › Forskning › peer review

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Martin Aumüller

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Dyk ned i detaljerne

DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search

Implementing Distributed Approximate Similarity Joins using Locality Sensitive Hashing

Representing Sparse Vectors with Differential Privacy, Low Error, Optimal Space, and Fast Access

Results of the NeurIPS’21 Challenge on Billion-Scale Approximate Nearest Neighbor Search

Sampling a Near Neighbor in High Dimensions — Who is the Fairest of Them All?