Results 1 to 10 of about 424 (215)
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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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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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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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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Determinantal Point Process as an alternative to NMS [PDF]
Proceedings of the British Machine Vision Conference 2020, 2020 Published in BMVC ...
Samik Some +2 more
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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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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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