Results 11 to 20 of about 438 (214)
Sparse Gaussian Processes on Discrete Domains
IEEE Access, 2021 Kernel methods on discrete domains have shown great promise for many challenging data types, for instance, biological sequence data and molecular structure data.
Vincent Fortuin +3 more
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Sensors, 2022 A pivotal topic in agriculture and food monitoring is the assessment of the quality and ripeness of agricultural products by using non-destructive testing techniques. Acoustic testing offers a rapid in situ analysis of the state of the agricultural good,
Dominique Albert-Weiss, Ahmad Osman
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The ASEP and Determinantal Point Processes [PDF]
Communications in Mathematical Physics, 2017 We introduce a family of discrete determinantal point processes related to orthogonal polynomials on the real line, with correlation kernels defined via spectral projections for the associated Jacobi matrices. For classical weights, we show how such ensembles arise as limits of various hypergeometric orthogonal polynomials ensembles. We then prove that
Borodin, Alexei, Olshanski, Grigori
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, 2016 Determinantal point processes are the main tool for the study of reflected Brownian motions. Thereby marginal distributions can be expressed in terms of Fredholm determinants, a form which is well suited for an asymptotic analysis. However, only partial aspects of the underlying theory of determinantal point processes is needed for our purposes and we ...
Thomas Weiss +2 more
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Testing Determinantal Point Processes
CoRR, 2020 Determinantal point processes (DPPs) are popular probabilistic models of diversity. In this paper, we investigate DPPs from a new perspective: property testing of distributions. Given sample access to an unknown distribution $q$ over the subsets of a ground set, we aim to distinguish whether $q$ is a DPP distribution, or $ε$-far from all DPP ...
Khashayar Gatmiry +2 more
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Kronecker Determinantal Point Processes
CoRR, 2016 Determinantal Point Processes (DPPs) are probabilistic models over all subsets a ground set of $N$ items. They have recently gained prominence in several applications that rely on "diverse" subsets. However, their applicability to large problems is still limited due to the $\mathcal O(N^3)$ complexity of core tasks such as sampling and learning.
Mariet, Zelda Elaine, Sra, Suvrit
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Determinantal Point Processes for Coresets
J. Mach. Learn. Res., 2018 When faced with a data set too large to be processed all at once, an obvious solution is to retain only part of it. In practice this takes a wide variety of different forms, and among them "coresets" are especially appealing. A coreset is a (small) weighted sample of the original data that comes with the following guarantee: a cost function can be ...
Tremblay, Nicolas +2 more
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On simulation of continuous determinantal point processes
Statistics and Computing, 2023 AbstractWe review how to simulate continuous determinantal point processes (DPPs) and improve the current simulation algorithms in several important special cases as well as detail how certain types of conditional simulation can be carried out. Importantly we show how to speed up the simulation of the widely used Fourier based projection DPPs, which ...
Frédéric Lavancier, Ege Rubak
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Markov Determinantal Point Processes
CoRR, 2012 Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012)
Raja Hafiz Affandi +2 more
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On a few statistical applications of determinantal point processes
ESAIM: Proceedings and Surveys, 2017 Determinantal point processes (DPPs) are a repulsive distribution over configurations of points. The 2016 conference Journées Modélisation Aléatoire et Statistique (MAS) of the French society for applied and industrial mathematics (SMAI) featured a ...
Bardenet Rémi +3 more
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