Convex Feature Learning for Multiple Targets via Output Structure Information
IEEE AccessMulti-target regression has gained popularity owing to its ability to predict multiple outcomes simultaneously, with improved performance over single-target methods.
S. Puhazholi, F. Sagayaraj Francis
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Omnipredictors for Regression and the Approximate Rank of Convex Functions
CoRRConsider the supervised learning setting where the goal is to learn to predict labels $\mathbf y$ given points $\mathbf x$ from a distribution. An \textit{omnipredictor} for a class $\mathcal L$ of loss functions and a class $\mathcal C$ of hypotheses is a predictor whose predictions incur less expected loss than the best hypothesis in $\mathcal C$ for
Parikshit Gopalan +4 more
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Convex Regression with a Penalty
CoRRA common way to estimate an unknown convex regression function $f_0: Ω\subset \mathbb{R}^d \rightarrow \mathbb{R}$ from a set of $n$ noisy observations is to fit a convex function that minimizes the sum of squared errors. However, this estimator is known for its tendency to overfit near the boundary of $Ω$, posing significant challenges in real-world ...
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A New Convex Estimator Combining Ridge and Ordinary Least Squares Estimators [PDF]
مجلة جامعة الانبار للعلوم الصرفةIn the presence of high correlation between the independent variables in the linear regression model, which is known as the multicollinearity problem, the ordinary least squares estimator produce large variations in the sample.
Karam Al-janabi, Mustafa Alheety
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Convex regression and its extensions to learning a Bregman divergence and difference of convex functions [PDF]
, 2022 Nonparametric convex regression has been extensively studied over the last two decades. It has been shown any Lipschitz convex function can be approximated with arbitrarily accuracy with a max of linear functions.
Siahkamari, Ali
core
Convex block-sparse linear regression with expanders -- provably
CoRR, 2016 Sparse matrices are favorable objects in machine learning and optimization. When such matrices are used, in place of dense ones, the overall complexity requirements in optimization can be significantly reduced in practice, both in terms of space and run-time.
Anastasios Kyrillidis +5 more
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BMC Musculoskeletal DisordersPurpose To explore the relationship between lumbosacral curve vertebral body tilt correction and postoperative coronal balance in adult degenerative scoliosis to determine the ideal target values for the tilt correction.
Zehua Jiang +10 more
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An Accelerated Successive Convex Approximation Scheme With Exact Step Sizes for L1-Regression
IEEE Open Journal of Signal ProcessingWe consider the minimization of $\ell _{1}$-regularized least-squares problems. A recent optimization approach uses successive convex approximations with an exact line search, which is highly competitive, especially in sparse problem instances. This work
Lukas Schynol +2 more
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Effects of Multilevel Facetectomy and Screw Density on Postoperative Changes in Spinal Rod Contour in Thoracic Adolescent Idiopathic Scoliosis Surgery. [PDF]
PLoS ONE, 2016 Flattening of the preimplantation rod contour in the sagittal plane influences thoracic kyphosis (TK) restoration in adolescent idiopathic scoliosis (AIS) surgery. The effects of multilevel facetectomy and screw density on postoperative changes in spinal
Terufumi Kokabu +5 more
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Multi-Trait Phenotypic Analysis and Biomass Estimation of Lettuce Cultivars Based on SFM-MVS
AgricultureTo address the problems of traditional methods that rely on destructive sampling, the poor adaptability of fixed equipment, and the susceptibility of single-view angle measurements to occlusions, a non-destructive and portable device for three ...
Tiezhu Li +6 more
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