Results 21 to 30 of about 426 (214)
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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Large deviations of the interference in the Ginibre network model
Stochastic Systems, 2014 Under different assumptions on the distribution of the fading random variables, we derive large deviation estimates for the tail of the interference in a wireless network model whose nodes are placed, over a bounded region of the plane, according to the ...
Giovanni Luca Torrisi, Emilio Leonardi
doaj +1 more source
Learning Determinantal Point Processes
CoRR, 2012 Determinantal point processes (DPPs), which arise in random matrix theory and quantum physics, are natural models for subset selection problems where diversity is preferred. Among many remarkable properties, DPPs offer tractable algorithms for exact inference, including computing marginal probabilities and sampling; however, an important open question ...
Alex Kulesza, Ben Taskar
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Wasserstein Learning of Determinantal Point Processes
CoRR, 2020 Determinantal point processes (DPPs) have received significant attention as an elegant probabilistic model for discrete subset selection. Most prior work on DPP learning focuses on maximum likelihood estimation (MLE). While efficient and scalable, MLE approaches do not leverage any subset similarity information and may fail to recover the true ...
Lucas Anquetil +4 more
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Spatio-Temporal Determinantal Point Processes
, 2023 9 pages, 1 ...
Vafaei, Nafiseh +3 more
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Learning Nonsymmetric Determinantal Point Processes
CoRR, 2019 Determinantal point processes (DPPs) have attracted substantial attention as an elegant probabilistic model that captures the balance between quality and diversity within sets. DPPs are conventionally parameterized by a positive semi-definite kernel matrix, and this symmetric kernel encodes only repulsive interactions between items.
Mike Gartrell +3 more
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Medication nonadherence during the first 100 days after pediatric hematopoietic stem cell transplantation (HSCT) and during oncology treatment increases risk for complications. BMT4me is a caregiver‐facing mobile health (mHealth) application providing medication reminders, symptom tracking, and note‐taking features to support ...
Micah A. Skeens +4 more
wiley +1 more source
Impact of Metastatic Patterns on Survival and Response to Therapy in Neuroblastoma
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background While the presence of metastases in neuroblastoma (NB) is a well‐established prognostic factor, the clinical significance of dissemination patterns and tumour burden and their impact on response and survival remains poorly understood.
Mariona Morell‐Daniel +15 more
wiley +1 more source
Determining Parental Factors for Clinical Trial Attrition in Pediatric Acute Lymphoblastic Leukemia
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background/Objectives Despite high enrollment rates on Children's Oncology Group (COG) protocols, attrition after initial consent is challenging, introducing bias and prolonging trial completion. While adult oncology literature has identified predictors of withdrawal, little is known about caregiver decision‐making for child participation in ...
Kimberly L. Stathas +3 more
wiley +1 more source
Partial isometries, duality, and determinantal point processes
Random Matrices: Theory and Applications, 2021 A determinantal point process (DPP) is an ensemble of random nonnegative-integer-valued Radon measures [Formula: see text] on a space S with measure [Formula: see text], whose correlation functions are all given by determinants specified by an integral kernel K called the correlation kernel.
Makoto Katori, Tomoyuki Shirai
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