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ORCIDRasmus Pagh
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- affiliation: University of Copenhagen, Denmark
- affiliation (former): IT University of Copenhagen, Denmark
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2020 – today
- 2024
[j34]Martin Aumüller, Christian Janos Lebeda, Boel Nelson, Rasmus Pagh:
PLAN: Variance-Aware Private Mean Estimation. Proc. Priv. Enhancing Technol. 2024(3): 606-625 (2024)- [c101]Kasper Green Larsen, Rasmus Pagh, Giuseppe Persiano, Toniann Pitassi, Kevin Yeo, Or Zamir:
Optimal Non-Adaptive Cell Probe Dictionaries and Hashing. ICALP 2024: 104:1-104:12 - [c100]Hao Wu, Rasmus Pagh:
Profile Reconstruction from Private Sketches. ICML 2024 - [c99]David Rasmussen Lolck, Rasmus Pagh
:
Shannon meets Gray: Noise-robust, Low-sensitivity Codes with Applications in Differential Privacy. SODA 2024: 1050-1066 - [c98]Ioana O. Bercea, Jakob Bæk Tejs Houen, Rasmus Pagh:
Daisy Bloom Filters. SWAT 2024: 9:1-9:19 - [c97]Ivan Damgård, Hannah Keller, Boel Nelson, Claudio Orlandi, Rasmus Pagh:
Differentially Private Selection from Secure Distributed Computing. WWW 2024: 1103-1114 - [i84]Niv Dayan, Ioana O. Bercea, Rasmus Pagh:
Aleph Filter: To Infinity in Constant Time. CoRR abs/2404.04703 (2024) - [i83]Hao Wu, Rasmus Pagh:
Profile Reconstruction from Private Sketches. CoRR abs/2406.01158 (2024) - [i82]Joel Daniel Andersson, Monika Henzinger, Rasmus Pagh, Teresa Anna Steiner, Jalaj Upadhyay:
Continual Counting with Gradual Privacy Expiration. CoRR abs/2406.03802 (2024) - 2023
- [j33]Niv Dayan, Ioana O. Bercea, Pedro Reviriego, Rasmus Pagh:
InfiniFilter: Expanding Filters to Infinity and Beyond. Proc. ACM Manag. Data 1(2): 140:1-140:27 (2023) - [c96]Praneeth Kacham, Rasmus Pagh, Mikkel Thorup, David P. Woodruff:
Pseudorandom Hashing for Space-bounded Computation with Applications in Streaming. FOCS 2023: 1515-1550 - [c95]Joel Daniel Andersson, Rasmus Pagh:
A Smooth Binary Mechanism for Efficient Private Continual Observation. NeurIPS 2023 - [c94]Jakob Bæk Tejs Houen, Rasmus Pagh, Stefan Walzer:
Simple Set Sketching. SOSA 2023: 228-241 - [i81]Praneeth Kacham, Rasmus Pagh, Mikkel Thorup, David P. Woodruff:
Pseudorandom Hashing for Space-bounded Computation with Applications in Streaming. CoRR abs/2304.06853 (2023) - [i80]David Rasmussen Lolck, Rasmus Pagh:
Shannon meets Gray: Noise-robust, Low-sensitivity Codes with Applications in Differential Privacy. CoRR abs/2305.02816 (2023) - [i79]Ivan Damgård, Hannah Keller, Boel Nelson, Claudio Orlandi, Rasmus Pagh:
Differentially Private Selection from Secure Distributed Computing. CoRR abs/2306.04564 (2023) - [i78]Martin Aumüller, Christian Janos Lebeda, Boel Nelson, Rasmus Pagh:
PLAN: Variance-Aware Private Mean Estimation. CoRR abs/2306.08745 (2023) - [i77]Joel Daniel Andersson, Rasmus Pagh:
A Smooth Binary Mechanism for Efficient Private Continual Observation. CoRR abs/2306.09666 (2023) - [i76]Kasper Green Larsen, Rasmus Pagh, Toniann Pitassi, Or Zamir:
Optimal Non-Adaptive Cell Probe Dictionaries and Hashing. CoRR abs/2308.16042 (2023) - [i75]Ivan Damgård, Hannah Keller, Boel Nelson, Claudio Orlandi, Rasmus Pagh:
Differentially Private Selection from Secure Distributed Computing. IACR Cryptol. ePrint Arch. 2023: 894 (2023) - 2022
- [j32]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) - [j31]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) - [j30]Rasmus Pagh:
Technical Perspective: Relative Error Streaming Quantiles. SIGMOD Rec. 51(1): 68 (2022) - [j29]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) - [c93]Matti Karppa, Martin Aumüller, Rasmus Pagh:
DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search. AISTATS 2022: 3108-3137 - [c92]Rasmus Pagh, Nina Mesing Stausholm:
Infinitely Divisible Noise in the Low Privacy Regime. ALT 2022: 881-909 - [c91]Matti Karppa, Rasmus Pagh:
HyperLogLogLog: Cardinality Estimation With One Log More. KDD 2022: 753-761 - [c90]Rasmus Pagh, Mikkel Thorup:
Improved Utility Analysis of Private CountSketch. NeurIPS 2022 - [i74]Rasmus Pagh, Mikkel Thorup:
Improved Utility Analysis of Private CountSketch. CoRR abs/2205.08397 (2022) - [i73]Matti Karppa, Rasmus Pagh:
HyperLogLogLog: Cardinality Estimation With One Log More. CoRR abs/2205.11327 (2022) - [i72]Ioana O. Bercea, Jakob Bæk Tejs Houen, Rasmus Pagh:
Daisy Bloom Filters. CoRR abs/2205.14894 (2022) - [i71]Jakob Bæk Tejs Houen, Rasmus Pagh, Stefan Walzer:
Simple Set Sketching. CoRR abs/2211.03683 (2022) - 2021
- [j28]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao:
Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 14(1-2): 1-210 (2021) - [j27]Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri:
Fair near neighbor search via sampling. SIGMOD Rec. 50(1): 42-49 (2021) - [c89]Martin Aumüller, Christian Janos Lebeda, Rasmus Pagh:
Differentially Private Sparse Vectors with Low Error, Optimal Space, and Fast Access. CCS 2021: 1223-1236 - [c88]Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, Ameya Velingker:
On the Power of Multiple Anonymous Messages: Frequency Estimation and Selection in the Shuffle Model of Differential Privacy. EUROCRYPT (3) 2021: 463-488 - [c87]Rasmus Pagh, Nina Mesing Stausholm:
Efficient Differentially Private F₀ Linear Sketching. ICDT 2021: 18:1-18:19 - [c86]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Amer Sinha:
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message. ICML 2021: 3692-3701 - [c85]Kasper Green Larsen, Rasmus Pagh, Jakub Tetek:
CountSketches, Feature Hashing and the Median of Three. ICML 2021: 6011-6020 - [c84]Angela Bonifati, Rasmus Pagh, Thomas Schwentick:
2021 ACM PODS Alberto O. Mendelzon Test-of-Time Award. PODS 2021: 82 - [e4]Petra Mutzel, Rasmus Pagh, Grzegorz Herman:
29th Annual European Symposium on Algorithms, ESA 2021, September 6-8, 2021, Lisbon, Portugal (Virtual Conference). LIPIcs 204, Schloss Dagstuhl - Leibniz-Zentrum für Informatik 2021, ISBN 978-3-95977-204-4 [contents] - [i70]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) - [i69]Kasper Green Larsen, Rasmus Pagh, Jakub Tetek:
CountSketches, Feature Hashing and the Median of Three. CoRR abs/2102.02193 (2021) - [i68]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh:
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead. CoRR abs/2106.04247 (2021) - [i67]Martin Aumüller, Christian Janos Lebeda, Rasmus Pagh:
Differentially private sparse vectors with low error, optimal space, and fast access. CoRR abs/2106.10068 (2021) - [i66]Matti Karppa, Martin Aumüller, Rasmus Pagh:
DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search. CoRR abs/2107.02736 (2021) - [i65]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Amer Sinha:
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message. CoRR abs/2109.13158 (2021) - [i64]Rasmus Pagh, Nina Mesing Stausholm:
Infinitely Divisible Noise in the Low Privacy Regime. CoRR abs/2110.06559 (2021) - 2020
- [j26]Yasuo Tabei, Yoshihiro Yamanishi, Rasmus Pagh:
Space-Efficient Feature Maps for String Alignment Kernels. Data Sci. Eng. 5(2): 168-179 (2020) - [c83]Badih Ghazi, Pasin Manurangsi, Rasmus Pagh, Ameya Velingker:
Private Aggregation from Fewer Anonymous Messages. EUROCRYPT (2) 2020: 798-827 - [c82]Rasmus Pagh, Johan Sivertsen:
The Space Complexity of Inner Product Filters. ICDT 2020: 22:1-22:14 - [c81]Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Ameya Velingker:
Pure Differentially Private Summation from Anonymous Messages. ITC 2020: 15:1-15:23 - [c80]Edith Cohen, Ofir Geri, Rasmus Pagh:
Composable Sketches for Functions of Frequencies: Beyond the Worst Case. ICML 2020: 2057-2067 - [c79]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh:
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead. ICML 2020: 3505-3514 - [c78]Edith Cohen, Rasmus Pagh, David P. Woodruff:
WOR and p's: Sketches for ℓp-Sampling Without Replacement. NeurIPS 2020 - [c77]Martin Aumüller, Rasmus Pagh, Francesco Silvestri:
Fair Near Neighbor Search: Independent Range Sampling in High Dimensions. PODS 2020: 191-204 - [c76]Mayank Goswami, Riko Jacob, Rasmus Pagh:
On the I/O Complexity of the k-Nearest Neighbors Problem. PODS 2020: 205-212 - [c75]Tobias Christiani, Rasmus Pagh, Mikkel Thorup:
Confirmation Sampling for Exact Nearest Neighbor Search. SISAP 2020: 97-110 - [c74]Thomas D. Ahle, Michael Kapralov, Jakob Bæk Tejs Knudsen, Rasmus Pagh, Ameya Velingker, David P. Woodruff, Amir Zandieh:
Oblivious Sketching of High-Degree Polynomial Kernels. SODA 2020: 141-160 - [e3]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] - [i63]Rasmus Pagh, Nina Mesing Stausholm:
Efficient Differentially Private F0 Linear Sketching. CoRR abs/2001.11932 (2020) - [i62]Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Ameya Velingker:
Pure Differentially Private Summation from Anonymous Messages. CoRR abs/2002.01919 (2020) - [i61]Mayank Goswami, Riko Jacob, Rasmus Pagh:
On the I/O complexity of the k-nearest neighbor problem. CoRR abs/2002.04870 (2020) - [i60]Edith Cohen, Ofir Geri, Rasmus Pagh:
Composable Sketches for Functions of Frequencies: Beyond the Worst Case. CoRR abs/2004.04772 (2020) - [i59]Edith Cohen, Rasmus Pagh, David P. Woodruff:
WOR and p's: Sketches for 𝓁p-Sampling Without Replacement. CoRR abs/2007.06744 (2020)
2010 – 2019
- 2019
- [c73]Martin Aumüller, Tobias Christiani, Rasmus Pagh, Michael Vesterli:
PUFFINN: Parameterless and Universally Fast FInding of Nearest Neighbors. ESA 2019: 10:1-10:16 - [c72]Rasmus Pagh, Nina Mesing Stausholm, Mikkel Thorup:
Hardness of Bichromatic Closest Pair with Jaccard Similarity. ESA 2019: 74:1-74:13 - [c71]Yasuo Tabei, Yoshihiro Yamanishi, Rasmus Pagh:
Space-Efficient Feature Maps for String Alignment Kernels. ICDM 2019: 1312-1317 - [r3]Rasmus Pagh:
Similarity Sketching. Encyclopedia of Big Data Technologies 2019 - [i58]Martin Aumüller, Rasmus Pagh, Francesco Silvestri:
Fair Near Neighbor Search: Independent Range Sampling in High Dimensions. CoRR abs/1906.01859 (2019) - [i57]Badih Ghazi, Rasmus Pagh, Ameya Velingker:
Scalable and Differentially Private Distributed Aggregation in the Shuffled Model. CoRR abs/1906.08320 (2019) - [i56]Martin Aumüller, Tobias Christiani, Rasmus Pagh, Michael Vesterli:
PUFFINN: Parameterless and Universally Fast FInding of Nearest Neighbors. CoRR abs/1906.12211 (2019) - [i55]Rasmus Pagh, Nina Stausholm, Mikkel Thorup:
Hardness of Bichromatic Closest Pair with Jaccard Similarity. CoRR abs/1907.02251 (2019) - [i54]Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, Ameya Velingker:
Private Heavy Hitters and Range Queries in the Shuffled Model. CoRR abs/1908.11358 (2019) - [i53]Michael Kapralov, Rasmus Pagh, Ameya Velingker, David P. Woodruff, Amir Zandieh:
Oblivious Sketching of High-Degree Polynomial Kernels. CoRR abs/1909.01410 (2019) - [i52]Rasmus Pagh, Johan Sivertsen:
The space complexity of inner product filters. CoRR abs/1909.10766 (2019) - [i51]Badih Ghazi, Pasin Manurangsi, Rasmus Pagh, Ameya Velingker:
Private Aggregation from Fewer Anonymous Messages. CoRR abs/1909.11073 (2019) - [i50]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao:
Advances and Open Problems in Federated Learning. CoRR abs/1912.04977 (2019) - [i49]Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, Ameya Velingker:
On the Power of Multiple Anonymous Messages. IACR Cryptol. ePrint Arch. 2019: 1382 (2019) - 2018
- [j25]Michael Mitzenmacher, Rasmus Pagh:
Simple multi-party set reconciliation. Distributed Comput. 31(6): 441-453 (2018) - [j24]Rasmus Pagh:
CoveringLSH: Locality-Sensitive Hashing without False Negatives. ACM Trans. Algorithms 14(3): 29:1-29:17 (2018) - [j23]Ha-Myung Park, Francesco Silvestri, Rasmus Pagh, Chin-Wan Chung, Sung-Hyon Myaeng, U Kang:
Enumerating Trillion Subgraphs On Distributed Systems. ACM Trans. Knowl. Discov. Data 12(6): 71:1-71:30 (2018) - [c70]Tobias Christiani, Rasmus Pagh, Johan Sivertsen:
Scalable and Robust Set Similarity Join. ICDE 2018: 1240-1243 - [c69]Samuel McCauley, Jesper W. Mikkelsen, Rasmus Pagh:
Set Similarity Search for Skewed Data. PODS 2018: 63-74 - [c68]Martin Aumüller, Tobias Christiani, Rasmus Pagh, Francesco Silvestri:
Distance-Sensitive Hashing. PODS 2018: 89-104 - [e2]Rasmus Pagh, Suresh Venkatasubramanian:
Proceedings of the Twentieth Workshop on Algorithm Engineering and Experiments, ALENEX 2018, New Orleans, LA, USA, January 7-8, 2018. SIAM 2018, ISBN 978-1-61197-505-5 [contents] - [i48]Yasuo Tabei, Yoshihiro Yamanishi, Rasmus Pagh:
Scalable Alignment Kernels via Space-Efficient Feature Maps. CoRR abs/1802.06382 (2018) - [i47]Samuel McCauley, Jesper W. Mikkelsen, Rasmus Pagh:
Set Similarity Search for Skewed Data. CoRR abs/1804.03054 (2018) - [i46]Djamal Belazzougui, Paolo Boldi, Rasmus Pagh, Sebastiano Vigna:
Fast Prefix Search in Little Space, with Applications. CoRR abs/1804.04720 (2018) - [i45]Tobias Christiani, Rasmus Pagh, Mikkel Thorup:
Confirmation Sampling for Exact Nearest Neighbor Search. CoRR abs/1812.02603 (2018) - 2017
- [j22]Rasmus Pagh, Ninh Pham, Francesco Silvestri, Morten Stöckel:
I/O-Efficient Similarity Join. Algorithmica 78(4): 1263-1283 (2017) - [j21]Leszek Gasieniec, Christos Levcopoulos, Andrzej Lingas, Rasmus Pagh, Takeshi Tokuyama:
Efficiently Correcting Matrix Products. Algorithmica 79(2): 428-443 (2017) - [j20]Rasmus Pagh, Francesco Silvestri, Johan Sivertsen, Matthew Skala:
Approximate furthest neighbor with application to annulus query. Inf. Syst. 64: 152-162 (2017) - [c67]Joachim Gudmundsson, Rasmus Pagh:
Range-Efficient Consistent Sampling and Locality-Sensitive Hashing for Polygons. ISAAC 2017: 42:1-42:13 - [c66]Rasmus Pagh:
Hardness and Approximation of High-Dimensional Search Problems (Invited Talk). MFCS 2017: 83:1-83:1 - [c65]Thomas D. Ahle, Martin Aumüller, Rasmus Pagh:
Parameter-free Locality Sensitive Hashing for Spherical Range Reporting. SODA 2017: 239-256 - [c64]Mayank Goswami, Rasmus Pagh, Francesco Silvestri, Johan Sivertsen:
Distance Sensitive Bloom Filters Without False Negatives. SODA 2017: 257-269 - [c63]Tobias Christiani, Rasmus Pagh:
Set similarity search beyond MinHash. STOC 2017: 1094-1107 - [i44]Joachim Gudmundsson, Rasmus Pagh:
Range-efficient consistent sampling and locality-sensitive hashing for polygons. CoRR abs/1701.05290 (2017) - [i43]Martin Aumüller, Tobias Christiani, Rasmus Pagh, Francesco Silvestri:
Distance-sensitive hashing. CoRR abs/1703.07867 (2017) - [i42]Tobias Christiani, Rasmus Pagh, Johan Sivertsen:
Scalable and robust set similarity join. CoRR abs/1707.06814 (2017) - [i41]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
- [j19]Laurent Bulteau, Vincent Froese, Konstantin Kutzkov, Rasmus Pagh:
Triangle Counting in Dynamic Graph Streams. Algorithmica 76(1): 259-278 (2016) - [c62]Ninh Pham, Rasmus Pagh:
Scalability and Total Recall with Fast CoveringLSH. CIKM 2016: 1109-1118 - [c61]Thomas Dybdahl Ahle, Rasmus Pagh, Ilya P. Razenshteyn, Francesco Silvestri:
On the Complexity of Inner Product Similarity Join. PODS 2016: 151-164 - [c60]Rasmus Pagh:
Locality-sensitive Hashing without False Negatives. SODA 2016: 1-9 - [e1]Rasmus Pagh:
15th Scandinavian Symposium and Workshops on Algorithm Theory, SWAT 2016, June 22-24, 2016, Reykjavik, Iceland. LIPIcs 53, Schloss Dagstuhl - Leibniz-Zentrum für Informatik 2016, ISBN 978-3-95977-011-8 [contents] - [r2]Rasmus Pagh:
Cuckoo Hashing. Encyclopedia of Algorithms 2016: 478-481 - [i40]Leszek Gasieniec, Christos Levcopoulos, Andrzej Lingas, Rasmus Pagh, Takeshi Tokuyama:
Efficiently Correcting Matrix Products. CoRR abs/1602.00435 (2016) - [i39]Ninh Pham, Rasmus Pagh:
Scalability and Total Recall with Fast CoveringLSH. CoRR abs/1602.02620 (2016) - [i38]Thomas D. Ahle, Martin Aumüller, Rasmus Pagh:
High-dimensional Spherical Range Reporting by Output-Sensitive Multi-Probing LSH. CoRR abs/1605.02673 (2016) - [i37]Mayank Goswami, Rasmus Pagh, Francesco Silvestri, Johan Sivertsen:
Distance Sensitive Bloom Filters Without False Negatives. CoRR abs/1607.05451 (2016) - [i36]Rasmus Pagh, Francesco Silvestri, Johan Sivertsen, Matthew Skala:
Approximate Furthest Neighbor with Application to Annulus Query. CoRR abs/1611.07303 (2016) - [i35]Tobias Christiani, Rasmus Pagh:
Set Similarity Search Beyond MinHash. CoRR abs/1612.07710 (2016) - 2015
- [c59]Rasmus Pagh, Ninh Pham, Francesco Silvestri, Morten Stöckel:
I/O-Efficient Similarity Join. ESA 2015: 941-952 - [c58]Rasmus Pagh:
Large-Scale Similarity Joins With Guarantees (Invited Talk). ICDT 2015: 15-24 - [c57]Rasmus Pagh, Francesco Silvestri, Johan Sivertsen, Matthew Skala:
Approximate Furthest Neighbor in High Dimensions. SISAP 2015: 3-14 - [c56]Mayank Goswami, Allan Grønlund Jørgensen, Kasper Green Larsen, Rasmus Pagh:
Approximate Range Emptiness in Constant Time and Optimal Space. SODA 2015: 769-775 - [c55]Tobias Christiani, Rasmus Pagh, Mikkel Thorup:
From Independence to Expansion and Back Again. STOC 2015: 813-820 - [i34]Rasmus Pagh, Morten Stöckel:
Association Rule Mining using Maximum Entropy. CoRR abs/1501.02143 (2015) - [i33]Tobias Christiani, Rasmus Pagh, Mikkel Thorup:
From Independence to Expansion and Back Again. CoRR abs/1506.03676 (2015) - [i32]Rasmus Pagh, Ninh Pham, Francesco Silvestri, Morten Stöckel:
I/O-Efficient Similarity Join. CoRR abs/1507.00552 (2015) - [i31]Rasmus Pagh:
Locality-sensitive Hashing without False Negatives. CoRR abs/1507.03225 (2015) - [i30]Thomas D. Ahle, Rasmus Pagh, Ilya P. Razenshteyn, Francesco Silvestri:
On the Complexity of Inner Product Similarity Join. CoRR abs/1510.02824 (2015) - 2014
- [j18]Po-Shen Loh, Rasmus Pagh:
Thresholds for Extreme Orientability. Algorithmica 69(3): 522-539 (2014)