Introduction to Linear Complexity Private Function Evaluation Is Practical
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Laconic Function Evaluation
Two Message Oblivious Evaluation of Cryptographic Functionalities
In-context learning of solution operators to linear elliptic PDEs – Frank Cole
Private Information Retrieval with Sublinear Online Time
Characterizing the Sample Complexity of Private Learners (CRCS Lunch Seminar)
Privacy and the Complexity of Simple Queries (CRCS Lunch Seminar)
[OOPSLA24] Sensitivity by Parametricity
IntroML @ ECE-UofT - Lecture 4 - Part II: PCA as Maximal Representation Variance
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Last Updated: September 29, 2026
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Marco Holz, Ágnes Kiss, Deevashwer Rathee, Thomas Schneider The 25th European Symposium on Research in Computer ... Paper by Yi Liu, Qi Wang, Siu-Ming Yiu presented at PKC 2022 See iacr.org/cryptodb/data/paper.php?pubkey=31729. Willy Quach (Northeastern University) Lattices: Algorithms, Nico Döttling and Nils Fleischhacker and Johannes Krupp and Dominique Schröder, Crypto 2016. IMA Data Science Seminar Speaker: Frank Cole (University of Minnesota) "In-context learning of solution operators to Paper by Henry Corrigan-Gibbs, Dmitry Kogan presented at Eurocrypt 2020 See ... CRCS Lunch Seminar (Monday, November 5, 2012) Speaker: Kobbi Nissim, Ben-Gurion University and Harvard CRCS Title: ... Sensitivity by Parametricity (Video, OOPSLA 2024) Elisabet Lobo-Vesga, Alejandro Russo, Marco Gaboardi, and Carlos Tomé ... We show that the learning problem is reduced to minimal recovery error or equivalently maximal representation variance.
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