In computer science, an output-sensitive algorithm is an algorithm whose running time depends on the size of the output, instead of, or in addition to, the size of the input. For certain problems where the output size varies widely, for example from linear in the size of the input to quadratic in the size of the input, analyses that take the output size explicitly into account can produce better runtime bounds that differentiate algorithms that would otherwise have identical asymptotic complexity. (Wikipedia).
Sensitivity (Electrical Engineering)
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This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
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This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
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Differential Privacy Symposium: Four Facets of Differential Privacy Saturday, November 12, 2016 https://www.ias.edu/differential-privacy More videos on http://video.ias.edu
From playlist Differential Privacy Symposium - November 12, 2016
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This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation
A Google TechTalk, presented by Lydia Zakynthinou, 2021/11/3 Privacy in MLSeminars - ABSTRACT: We present two differentially private mean estimators for multivariate (sub)Gaussian distributions with unknown covariance. Our estimators are accurate with respect to the Mahalanobis loss and
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From playlist The CERIAS Security Seminars 2009
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Differential privacy is a robust mathematical framework for designing privacy-preserving computations on sensitive data. In this tutorial we will cover the key definitions and intuitions behind differential privacy and introduce the core building blocks used by most differentially private
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A Google TechTalk, presented by Ruihan Wu, 2022/08/31 Differential Privacy for ML Series.
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Comparing Iterative and Recursive Factorial Functions
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