XGBoost
Tree boosting is a highly effective and widely used machine learning method.\nIn this paper, we describe a scalable end-to-end tree boosting system called\nXGBoost, which is use...
Publications
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387 publications
Glove: Global Vectors for Word Representation
Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the o...
An Algorithm for Least-Squares Estimation of Nonlinear Parameters
Previous article Next article An Algorithm for Least-Squares Estimation of Nonlinear ParametersDonald W. MarquardtDonald W. Marquardthttps://doi.org/10.1137/0111030PDFPDF PLUSBi...
Focal Loss for Dense Object Detection
The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations...
Compressed sensing
Suppose x is an unknown vector in Ropf <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> (a digital image or signal); we pla...
Numerical recipes in C: the art of scientific computing
From the Publisher: This is the revised and greatly expanded Second Edition of the hugely popular Numerical Recipes: The Art of Scientific Computing. The product of a unique co...
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
We make an analogy between images and statistical mechanics systems. Pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a ...
Compressed sensing
Suppose x is an unknown vector in Ropfm (a digital image or signal); we plan to measure n general linear functionals of x and then reconstruct. If x is known to be compressible ...
Regularization Paths for Generalized Linear Models via Coordinate Descent
We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nom...
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
This paper considers the model problem of reconstructing an object from incomplete frequency samples. Consider a discrete-time signal f ∈ C N and a randomly chosen set of freque...
Regularization Paths for Generalized Linear Models via Coordinate Descent.
We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multinomia...
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
We consider the class of iterative shrinkage-thresholding algorithms (ISTA) for solving linear inverse problems arising in signal/image processing. This class of methods, which ...