Zobrazeno 1 - 10
of 520
pro vyhledávání: '"Aravkin, Aleksandr"'
Publikováno v:
Open Journal of Mathematical Optimization, Vol 2, Iss , Pp 1-18 (2021)
Piecewise Linear-Quadratic (PLQ) penalties are widely used to develop models in statistical inference, signal processing, and machine learning. Common examples of PLQ penalties include least squares, Huber, Vapnik, 1-norm, and their asymmetric genera
Externí odkaz:
https://doaj.org/article/4600d2aa6ab4462d96ccbb93333d306f
Determining gene regulatory network (GRN) structure is a central problem in biology, with a variety of inference methods available for different types of data. For a widely prevalent and challenging use case, namely single-cell gene expression data m
Externí odkaz:
http://arxiv.org/abs/2407.00754
Identifying Ordinary Differential Equations (ODEs) from measurement data requires both fitting the dynamics and assimilating, either implicitly or explicitly, the measurement data. The Sparse Identification of Nonlinear Dynamics (SINDy) method involv
Externí odkaz:
http://arxiv.org/abs/2405.03154
Meta-analysis allows rigorous aggregation of estimates and uncertainty across multiple studies. When a given study reports multiple estimates, such as log odds ratios (ORs) or log relative risks (RRs) across exposure groups, accounting for within-stu
Externí odkaz:
http://arxiv.org/abs/2404.11678
Autor:
Zheng, Peng, Worku, Nahom, Bannick, Marlena, Dielemann, Joseph, Weaver, Marcia, Murray, Christopher, Aravkin, Aleksandr
Benchmarking tools, including stochastic frontier analysis (SFA), data envelopment analysis (DEA), and its stochastic extension (StoNED) are core tools in economics used to estimate an efficiency envelope and production inefficiencies from data. The
Externí odkaz:
http://arxiv.org/abs/2404.04301
Efficient representations of data are essential for processing, exploration, and human understanding, and Principal Component Analysis (PCA) is one of the most common dimensionality reduction techniques used for the analysis of large, multivariate da
Externí odkaz:
http://arxiv.org/abs/2311.01826
Autor:
Pillonetto, Gianluigi, Aravkin, Aleksandr, Gedon, Daniel, Ljung, Lennart, Ribeiro, Antônio H., Schön, Thomas B.
Deep learning is a topic of considerable current interest. The availability of massive data collections and powerful software resources has led to an impressive amount of results in many application areas that reveal essential but hidden properties o
Externí odkaz:
http://arxiv.org/abs/2301.12832
We develop a Levenberg-Marquardt method for minimizing the sum of a smooth nonlinear least-squar es term $f(x) = \tfrac{1}{2} \|F(x)\|_2^2$ and a nonsmooth term $h$. Both $f$ and $h$ may be nonconvex. Steps are computed by minimizing the sum of a reg
Externí odkaz:
http://arxiv.org/abs/2301.02347
Spatiotemporal data is increasingly available due to emerging sensor and data acquisition technologies that track moving objects. Spatiotemporal clustering addresses the need to efficiently discover patterns and trends in moving object behavior witho
Externí odkaz:
http://arxiv.org/abs/2211.05337
Linear Mixed-Effects (LME) models are a fundamental tool for modeling clustered data, including cohort studies, longitudinal data analysis, and meta-analysis. The design and analysis of variable selection methods for LMEs is considerably more difficu
Externí odkaz:
http://arxiv.org/abs/2209.10575