Rasmus Pagh

ORCID

Publication Activity (10 Years)

Years Active: 1999-2024
Publications (10 Years): 107

Top Venues

Publications

Ioana O. Bercea, Jakob Bæk Tejs Houen, Rasmus Pagh
Daisy Bloom Filters. SWAT (2024)
Martin Aumüller, Christian Janos Lebeda, Boel Nelson, Rasmus Pagh
PLAN: Variance-Aware Private Mean Estimation. Proc. Priv. Enhancing Technol. 2024 (3) (2024)
David Rasmussen Lolck, Rasmus Pagh
Shannon meets Gray: Noise-robust, Low-sensitivity Codes with Applications in Differential Privacy. SODA (2024)
Hao Wu, Rasmus Pagh
Profile Reconstruction from Private Sketches. CoRR (2024)
Kasper Green Larsen, Rasmus Pagh, Giuseppe Persiano, Toniann Pitassi, Kevin Yeo, Or Zamir
Optimal Non-Adaptive Cell Probe Dictionaries and Hashing. ICALP (2024)
Niv Dayan, Ioana O. Bercea, Rasmus Pagh
Aleph Filter: To Infinity in Constant Time. CoRR (2024)
Ivan Damgård, Hannah Keller, Boel Nelson, Claudio Orlandi, Rasmus Pagh
Differentially Private Selection from Secure Distributed Computing. WWW (2024)
Joel Daniel Andersson, Monika Henzinger, Rasmus Pagh, Teresa Anna Steiner, Jalaj Upadhyay
Continual Counting with Gradual Privacy Expiration. CoRR (2024)
David Rasmussen Lolck, Rasmus Pagh
Shannon meets Gray: Noise-robust, Low-sensitivity Codes with Applications in Differential Privacy. CoRR (2023)
Niv Dayan, Ioana O. Bercea, Pedro Reviriego, Rasmus Pagh
InfiniFilter: Expanding Filters to Infinity and Beyond. Proc. ACM Manag. Data 1 (2) (2023)
Praneeth Kacham, Rasmus Pagh, Mikkel Thorup, David P. Woodruff
Pseudorandom Hashing for Space-bounded Computation with Applications in Streaming. CoRR (2023)
Joel Daniel Andersson, Rasmus Pagh
A Smooth Binary Mechanism for Efficient Private Continual Observation. CoRR (2023)
Martin Aumüller, Christian Janos Lebeda, Boel Nelson, Rasmus Pagh
PLAN: Variance-Aware Private Mean Estimation. CoRR (2023)
Ivan Damgård, Hannah Keller, Boel Nelson, Claudio Orlandi, Rasmus Pagh
Differentially Private Selection from Secure Distributed Computing. CoRR (2023)
Praneeth Kacham, Rasmus Pagh, Mikkel Thorup, David P. Woodruff
Pseudorandom Hashing for Space-bounded Computation with Applications in Streaming. FOCS (2023)
Kasper Green Larsen, Rasmus Pagh, Toniann Pitassi, Or Zamir
Optimal Non-Adaptive Cell Probe Dictionaries and Hashing. CoRR (2023)
Joel Daniel Andersson, Rasmus Pagh
A Smooth Binary Mechanism for Efficient Private Continual Observation. NeurIPS (2023)
Jakob Bæk Tejs Houen, Rasmus Pagh, Stefan Walzer
Simple Set Sketching. SOSA (2023)
Ivan Damgård, Hannah Keller, Boel Nelson, Claudio Orlandi, Rasmus Pagh
Differentially Private Selection from Secure Distributed Computing. IACR Cryptol. ePrint Arch. 2023 (2023)
Matti Karppa, Rasmus Pagh
HyperLogLogLog: Cardinality Estimation With One Log More. KDD (2022)
Rasmus Pagh, Nina Mesing Stausholm
Infinitely Divisible Noise in the Low Privacy Regime. ALT (2022)
Matti Karppa, Martin Aumüller, Rasmus Pagh
DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search. AISTATS (2022)
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)
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) (2022)
Rasmus Pagh
Technical Perspective: Relative Error Streaming Quantiles. SIGMOD Rec. 51 (1) (2022)
Rasmus Pagh, Mikkel Thorup
Improved Utility Analysis of Private CountSketch. CoRR (2022)
Ioana O. Bercea, Jakob Bæk Tejs Houen, Rasmus Pagh
Daisy Bloom Filters. CoRR (2022)
Rasmus Pagh, Mikkel Thorup
Improved Utility Analysis of Private CountSketch. NeurIPS (2022)
Jakob Bæk Tejs Houen, Rasmus Pagh, Stefan Walzer
Simple Set Sketching. CoRR (2022)
Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri
Sampling near neighbors in search for fairness. Commun. ACM 65 (8) (2022)
Matti Karppa, Rasmus Pagh
HyperLogLogLog: Cardinality Estimation With One Log More. CoRR (2022)
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)
Martin Aumüller, Christian Janos Lebeda, Rasmus Pagh
Differentially private sparse vectors with low error, optimal space, and fast access. CoRR (2021)
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 (2021)
Kasper Green Larsen, Rasmus Pagh, Jakub Tetek
CountSketches, Feature Hashing and the Median of Three. ICML (2021)
Angela Bonifati, Rasmus Pagh, Thomas Schwentick
2021 ACM PODS Alberto O. Mendelzon Test-of-Time Award. PODS (2021)
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 (2021)
Kasper Green Larsen, Rasmus Pagh, Jakub Tetek
CountSketches, Feature Hashing and the Median of Three. CoRR (2021)
Rasmus Pagh, Nina Mesing Stausholm
Efficient Differentially Private F₀ Linear Sketching. ICDT (2021)
Matti Karppa, Martin Aumüller, Rasmus Pagh
DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search. CoRR (2021)
Rasmus Pagh, Nina Mesing Stausholm
Infinitely Divisible Noise in the Low Privacy Regime. CoRR (2021)
Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri
Fair near neighbor search via sampling. SIGMOD Rec. 50 (1) (2021)
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)
Martin Aumüller, Christian Janos Lebeda, Rasmus Pagh
Differentially Private Sparse Vectors with Low Error, Optimal Space, and Fast Access. CCS (2021)
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead. CoRR (2021)
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) (2021)
Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Ameya Velingker
Pure Differentially Private Summation from Anonymous Messages. CoRR (2020)
Martin Aumüller, Rasmus Pagh, Francesco Silvestri
Fair Near Neighbor Search: Independent Range Sampling in High Dimensions. PODS (2020)
Edith Cohen, Rasmus Pagh, David P. Woodruff
-Sampling Without Replacement. CoRR (2020)
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)
Mayank Goswami, Riko Jacob, Rasmus Pagh
On the I/O complexity of the k-nearest neighbor problem. CoRR (2020)
Edith Cohen, Rasmus Pagh, David P. Woodruff
-Sampling Without Replacement. NeurIPS (2020)
Edith Cohen, Ofir Geri, Rasmus Pagh
Composable Sketches for Functions of Frequencies: Beyond the Worst Case. CoRR (2020)
Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Ameya Velingker
Pure Differentially Private Summation from Anonymous Messages. ITC (2020)
Rasmus Pagh, Nina Mesing Stausholm
Linear Sketching. CoRR (2020)
Yasuo Tabei, Yoshihiro Yamanishi, Rasmus Pagh
Space-Efficient Feature Maps for String Alignment Kernels. Data Sci. Eng. 5 (2) (2020)
Mayank Goswami, Riko Jacob, Rasmus Pagh
On the I/O Complexity of the k-Nearest Neighbors Problem. PODS (2020)
Badih Ghazi, Pasin Manurangsi, Rasmus Pagh, Ameya Velingker
Private Aggregation from Fewer Anonymous Messages. EUROCRYPT (2) (2020)
Edith Cohen, Ofir Geri, Rasmus Pagh
Composable Sketches for Functions of Frequencies: Beyond the Worst Case. ICML (2020)
Tobias Christiani, Rasmus Pagh, Mikkel Thorup
Confirmation Sampling for Exact Nearest Neighbor Search. SISAP (2020)
Rasmus Pagh, Johan Sivertsen
The Space Complexity of Inner Product Filters. ICDT (2020)
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead. ICML (2020)
Michael Kapralov, Rasmus Pagh, Ameya Velingker, David P. Woodruff, Amir Zandieh
Oblivious Sketching of High-Degree Polynomial Kernels. CoRR (2019)
Badih Ghazi, Rasmus Pagh, Ameya Velingker
Scalable and Differentially Private Distributed Aggregation in the Shuffled Model. CoRR (2019)
Martin Aumüller, Rasmus Pagh, Francesco Silvestri
Fair Near Neighbor Search: Independent Range Sampling in High Dimensions. CoRR (2019)
Badih Ghazi, Pasin Manurangsi, Rasmus Pagh, Ameya Velingker
Private Aggregation from Fewer Anonymous Messages. CoRR (2019)
Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, Ameya Velingker
Private Heavy Hitters and Range Queries in the Shuffled Model. CoRR (2019)
Yasuo Tabei, Yoshihiro Yamanishi, Rasmus Pagh
Space-Efficient Feature Maps for String Alignment Kernels. ICDM (2019)
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 (2019)
Rasmus Pagh, Nina Stausholm, Mikkel Thorup
Hardness of Bichromatic Closest Pair with Jaccard Similarity. CoRR (2019)
Martin Aumüller, Tobias Christiani, Rasmus Pagh, Michael Vesterli
PUFFINN: Parameterless and Universally Fast FInding of Nearest Neighbors. CoRR (2019)
Rasmus Pagh, Nina Mesing Stausholm, Mikkel Thorup
Hardness of Bichromatic Closest Pair with Jaccard Similarity. ESA (2019)
Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, Ameya Velingker
On the Power of Multiple Anonymous Messages. IACR Cryptol. ePrint Arch. 2019 (2019)
Rasmus Pagh, Johan Sivertsen
The space complexity of inner product filters. CoRR (2019)
Martin Aumüller, Tobias Christiani, Rasmus Pagh, Michael Vesterli
PUFFINN: Parameterless and Universally Fast FInding of Nearest Neighbors. ESA (2019)
Samuel McCauley, Jesper W. Mikkelsen, Rasmus Pagh
Set Similarity Search for Skewed Data. PODS (2018)
Tobias Christiani, Rasmus Pagh, Johan Sivertsen
Scalable and Robust Set Similarity Join. ICDE (2018)
Michael Mitzenmacher, Rasmus Pagh
Simple multi-party set reconciliation. Distributed Comput. 31 (6) (2018)
Yasuo Tabei, Yoshihiro Yamanishi, Rasmus Pagh
Scalable Alignment Kernels via Space-Efficient Feature Maps. CoRR (2018)
Tobias Christiani, Rasmus Pagh, Mikkel Thorup
Confirmation Sampling for Exact Nearest Neighbor Search. CoRR (2018)
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) (2018)
Djamal Belazzougui, Paolo Boldi, Rasmus Pagh, Sebastiano Vigna
Fast Prefix Search in Little Space, with Applications. CoRR (2018)
Samuel McCauley, Jesper W. Mikkelsen, Rasmus Pagh
Set Similarity Search for Skewed Data. CoRR (2018)
Rasmus Pagh
CoveringLSH: Locality-Sensitive Hashing without False Negatives. ACM Trans. Algorithms 14 (3) (2018)
Martin Aumüller, Tobias Christiani, Rasmus Pagh, Francesco Silvestri
Distance-Sensitive Hashing. PODS (2018)
Mayank Goswami, Rasmus Pagh, Francesco Silvestri, Johan Sivertsen
Distance Sensitive Bloom Filters Without False Negatives. SODA (2017)
Rasmus Pagh
Hardness and Approximation of High-Dimensional Search Problems (Invited Talk). MFCS (2017)
Joachim Gudmundsson, Rasmus Pagh
Range-Efficient Consistent Sampling and Locality-Sensitive Hashing for Polygons. ISAAC (2017)
Joachim Gudmundsson, Rasmus Pagh
Range-efficient consistent sampling and locality-sensitive hashing for polygons. CoRR (2017)
Tobias Christiani, Rasmus Pagh
Set similarity search beyond MinHash. STOC (2017)
Thomas D. Ahle, Martin Aumüller, Rasmus Pagh
Parameter-free Locality Sensitive Hashing for Spherical Range Reporting. SODA (2017)
Rasmus Pagh, Francesco Silvestri, Johan Sivertsen, Matthew Skala
Approximate furthest neighbor with application to annulus query. Inf. Syst. 64 (2017)
Martin Dietzfelbinger, Michael Mitzenmacher, Rasmus Pagh, David P. Woodruff, Martin Aumüller
Theory and Applications of Hashing (Dagstuhl Seminar 17181). Dagstuhl Reports 7 (5) (2017)
Martin Aumüller, Tobias Christiani, Rasmus Pagh, Francesco Silvestri
Distance-sensitive hashing. CoRR (2017)
Rasmus Pagh, Ninh Pham, Francesco Silvestri, Morten Stöckel
I/O-Efficient Similarity Join. Algorithmica 78 (4) (2017)
Leszek Gasieniec, Christos Levcopoulos, Andrzej Lingas, Rasmus Pagh, Takeshi Tokuyama
Efficiently Correcting Matrix Products. Algorithmica 79 (2) (2017)
Tobias Christiani, Rasmus Pagh, Johan Sivertsen
Scalable and robust set similarity join. CoRR (2017)
Mayank Goswami, Rasmus Pagh, Francesco Silvestri, Johan Sivertsen
Distance Sensitive Bloom Filters Without False Negatives. CoRR (2016)
Rasmus Pagh
Locality-sensitive Hashing without False Negatives. SODA (2016)
Thomas Dybdahl Ahle, Rasmus Pagh, Ilya P. Razenshteyn, Francesco Silvestri
On the Complexity of Inner Product Similarity Join. PODS (2016)

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Publication Activity (10 Years)

Top Topics

Top Venues

Rasmus Pagh