Results 21 to 30 of about 57,258 (313)
, 2023 We develop a framework for efficiently transforming certain approximation algorithms into differentially-private variants, in a black-box manner. Specifically, our results focus on algorithms A that output an approximation to a function f of the form $(1-
Blocki, Jeremiah +3 more
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Approximate Implicitization Using Linear Algebra
Journal of Applied Mathematics, 2012 We consider a family of algorithms for approximate implicitization of rational parametric curves and surfaces. The main approximation tool in all of the approaches is the singular value decomposition, and they are therefore well suited to floating-point ...
Oliver J. D. Barrowclough, Tor Dokken
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Convex Analysis for Minimizing and Learning Submodular Set Functions [PDF]
, 2013 The connections between convexity and submodularity are explored, for purposes of minimizing and learning submodular set functions. First, we develop a novel method for minimizing a particular class of submodular functions, which can be expressed as a
Peter Stobbe, Stobbe, Peter
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IEEE Access, 2018 Reinforcement learning (RL) is an important machine learning paradigm that can be used for learning from the data obtained by the human-computer interface and the interaction in human-centered smart systems. One of the essential problems in RL algorithms
Dazi Li +3 more
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A sample decreasing threshold greedy-based algorithm for big data summarisation
Journal of Big Data, 2021 As the scale of datasets used for big data applications expands rapidly, there have been increased efforts to develop faster algorithms. This paper addresses big data summarisation problems using the submodular maximisation approach and proposes an ...
Teng Li +2 more
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Quantum and classical algorithms for approximate submodular function minimization [PDF]
Quantum Information and Computation, 2019 Submodular functions are set functions mapping every subset of some ground set of size n into the real numbers and satisfying the diminishing returns property. Submodular minimization is an important field in discrete optimization theory due to its relevance for various branches of mathematics, computer science and economics.
Yassine Hamoudi +3 more
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Efficient approximation of random fields for numerical applications [PDF]
, 2014 This article is dedicated to the rapid computation of separable expansions for the approximation of random fields. We consider approaches based on techniques from the approximation of non-local operators on the one hand and based on the pivoted Cholesky ...
Michael Peters +5 more
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Machines, 2021 In this paper, we consider the problem of selecting the most efficient optimization algorithm for neural network approximation—solving optimal control problems with mixed constraints.
Irina Bolodurina, Lyubov Zabrodina
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Monotone Submodular Maximization over a Matroid via Non-Oblivious Local Search [PDF]
, 2013 We present an optimal, combinatorial 1−1/e approximation algorithm for monotone submodular optimization over a matroid constraint. Compared to the continuous greedy algorithm (Calinescu, Chekuri, Pál and Vondrák, 2008), our algorithm is extremely simple ...
Filmus, Yuval +3 more
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Efficient by Precision Algorithms for Approximating Functions from Some Classes by Fourier Series
Кібернетика та комп'ютерні технологіїIntroduction. The problem of approximation can be considered as the basis of computational methods, namely, the approximation of individual functions or classes of functions by functions that are in some sense simpler than the functions being ...
Olena Kolomys
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