Randomized Gradient Boosting Machine

DOI10.1137/18M1223277zbMath1451.90122arXiv1810.10158OpenAlexW3091997330MaRDI QIDQ4971024

Haihao Lu, Rahul Mazumder

Publication date: 8 October 2020

Published in: SIAM Journal on Optimization (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1810.10158

zbMATH Keywords

convex optimizationensemble methodscomputational guaranteescoordinate descentgradient boostingfirst order methods

Mathematics Subject Classification ID

Convex programming (90C25) General topics in computing methodologies (68U01)

Related Items (3)

Temporal mixture ensemble models for probabilistic forecasting of intraday cryptocurrency volume ⋮ Driving detection based on the multifeature fusion ⋮ A new accelerated proximal boosting machine with convergence rate \(O(1/t^2)\)

Uses Software

• LIBSVM
• Scikit
• AdaBoost.MH
• randomForest
• ElemStatLearn
• TensorFlow
• MLlib
• XGBoost
• LightGBM
• Apache Spark
• L0Learn

Cites Work

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• Greedy function approximation: A gradient boosting machine.
• A new perspective on boosting in linear regression via subgradient optimization and relatives
• SLOPE-adaptive variable selection via convex optimization
• Error bounds and convergence analysis of feasible descent methods: A general approach
• On the convergence of the coordinate descent method for convex differentiable minimization
• A decision-theoretic generalization of on-line learning and an application to boosting
• Condition number complexity of an elementary algorithm for computing a reliable solution of a conic linear system
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Worst-case complexity of cyclic coordinate descent: \(O(n^2)\) gap with randomized version
• Coordinate descent algorithms
• Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function
• Boosting with early stopping: convergence and consistency
• Towards a deeper geometric, analytic and algorithmic understanding of margins
• Efficiency of Coordinate Descent Methods on Huge-Scale Optimization Problems
• The Rate of Convergence of AdaBoost
• Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms
• A Primal-Dual Convergence Analysis of Boosting
• On the Convergence of Block Coordinate Descent Type Methods
• A new condition number for linear programming
• Stochastic gradient boosting.
• Logistic regression, AdaBoost and Bregman distances

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