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Conditional mean embedding and optimal feature selection via positive definite kernels [PDF]
Opuscula Mathematica, 2023 Motivated by applications, we consider new operator-theoretic approaches to conditional mean embedding (CME). Our present results combine a spectral analysis-based optimization scheme with the use of kernels, stochastic processes, and constructive ...
Palle E.T. Jorgensen +2 more
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Frontiers in Medicine, 2022 BackgroundObtaining an ideal vault is crucial in the implantable collamer lens (ICL) surgery. Prediction of the vault value is difficult since it requires the integration of multiple factors.
Zhikun Yang +7 more
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Deep Successive Convex Approximation for Image Super-Resolution
Mathematics, 2023 Image super-resolution (SR), as one of the classic image processing issues, has attracted increasing attention from researchers. As a highly ill-conditioned, non-convex optimization issue, it is difficult for image SR to restore a high-resolution (HR ...
Xiaohui Li, Jinpeng Wang, Xinbo Liu
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A Convex Framework for Fair Regression
CoRR, 2017 We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems. These regularizers all enjoy convexity, permitting fast optimization, and they span the rang from notions of group fairness to strong individual fairness.
Richard Berk +7 more
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Adaptive Sampling for Convex Regression
CoRR, 2018 In this paper, we introduce the first principled adaptive-sampling procedure for learning a convex function in the $L_\infty$ norm, a problem that arises often in the behavioral and social sciences. We present a function-specific measure of complexity and use it to prove that, for each convex function $f_{\star}$, our algorithm nearly attains the ...
Max Simchowitz +3 more
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Multivariate convex regression with adaptive partitioning
J. Mach. Learn. Res., 2011 We propose a new, nonparametric method for multivariate regression subject to convexity or concavity constraints on the response function. Convexity constraints are common in economics, statistics, operations research, financial engineering and optimization, but there is currently no multivariate method that is computationally feasible for more than a ...
Lauren Hannah, David B. Dunson
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Wind Speed Interval Prediction Based on the Hybrid Ensemble Model With Biased Convex Cost Function
Frontiers in Energy Research, 2022 This study proposes a combination interval prediction based hybrid ensemble (CIPE) model for short-term wind speed prediction. The combination interval prediction (CIP) model employs the extreme learning machine (ELM) as the predictor with a biased ...
Huan Long, Runhao Geng, Chen Zhang
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The Learning Rates of Regularized Regression Based on Reproducing Kernel Banach Spaces
Abstract and Applied Analysis, 2013 We study the convergence behavior of regularized regression based on reproducing kernel Banach spaces (RKBSs). The convex inequality of uniform convex Banach spaces is used to show the robustness of the optimal solution with respect to the distributions.
Baohuai Sheng, Peixin Ye
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Journal of Inequalities and Applications, 2021 In this work, we introduce a new accelerated algorithm using a linesearch technique for solving convex minimization problems in the form of a summation of two lower semicontinuous convex functions.
Panitarn Sarnmeta +3 more
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Twin Support Vector Regression Model Based on Heteroscedastic Gaussian Noise and Its Application
IEEE Access, 2022 The main purpose of twin support vector regression (TSVR) is to find linear or nonlinear relationships in sample data, and then predict future data. TSVR is the decomposition of a large convex quadratic programming problem into two small convex quadratic
Shiguang Zhang +3 more
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