Results 1 to 10 of about 327 (115)
Determinantal Point Processes for Image Processing [PDF]
SIAM Journal on Imaging Sciences, 2021 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Claire Launay
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Improved clinical data imputation via classical and quantum determinantal point processes [PDF]
eLifeImputing data is a critical issue for machine learning practitioners, including in the life sciences domain, where missing clinical data is a typical situation and the reliability of the imputation is of great importance. Currently, there is no canonical
Skander Kazdaghli +3 more
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Monte Carlo with determinantal point processes [PDF]
Annals of Applied Probability, 2020 58 pages, 6 figures.
Rémi Bardenet
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Determinantal Point Processes for Machine Learning [PDF]
Foundations and Trends in Machine Learning, 2012 Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory. In contrast to traditional structured models like Markov random fields, which become intractable and hard to approximate in the presence of negative correlations, DPPs offer efficient and exact algorithms for ...
Ben Taskar, Taskar Ben
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Determinantal point processes in the flat limit
Bernoulli, 2023 Determinantal point processes (DPPs) are repulsive point processes where the interaction between points depends on the determinant of a positive-semi definite matrix. In this paper, we study the limiting process of L-ensembles based on kernel matrices, when the kernel function becomes flat (so that every point interacts with every other point, in a ...
Nicolas Tremblay
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Quasi-symmetries of determinantal point processes [PDF]
Annals of Probability, 2018 The main result of this paper is that determinantal point processes on the real line corresponding to projection operators with integrable kernels are quasi-invariant, in the continuous case, under the group of diffeomorphisms with compact support (Theorem 1.4); in the discrete case, under the group of all finite permutations of the phase space ...
Alexander Bufetov
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Conditional Measures of Determinantal Point Processes [PDF]
Functional Analysis and Its Applications, 2020 17 ...
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Optimal transport between determinantal point processes and application to fast simulation
Modern Stochastics: Theory and Applications, 2021 Two optimal transport problems between determinantal point processes (DPP for short) are investigated. It is shown how to estimate the Kantorovitch–Rubinstein and Wasserstein-2 distances between distributions of DPP. These results are applied to evaluate
Laurent Decreusefond, Guillaume Moroz
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A Novel Ensemble Strategy Based on Determinantal Point Processes for Transfer Learning
Mathematics, 2022 Transfer learning (TL) hopes to train a model for target domain tasks by using knowledge from different but related source domains. Most TL methods focus more on improving the predictive performance of the single model across domains.
Ying Lv +3 more
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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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