Results 21 to 30 of about 24,904 (254)
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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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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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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Isotonic and Convex Regression: A Review of Theory, Algorithms, and Applications
MathematicsShape-restricted regression provides a flexible framework for estimating an unknown relationship between input variables and a response when little is known about the functional form, but qualitative structural information is available. In many practical
Eunji Lim
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An Efficient Minimax Optimal Estimator For Multivariate Convex Regression
CoRR, 2022 This work studies the computational aspects of multivariate convex regression in dimensions $d \ge 5$. Our results include the \emph{first} estimators that are minimax optimal (up to logarithmic factors) with polynomial runtime in the sample size for both $L$-Lipschitz convex regression, and $Γ$-bounded convex regression under polytopal support.
Gil Kur, Eli Putterman
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Journal of Statistical Software, 2014 Convex optimization now plays an essential role in many facets of statistics. We briefly survey some recent developments and describe some implementations of these methods in R .
Roger Koenker, Ivan Mizera
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