Foundations and Trends® in Machine Learning(共66册), 这套丛书还有 《Kernels for Vector-Valued Functions》《Model-based Reinforcement Learning》《A Friendly Tutorial on Mean-Field Spin Glass Techniques for Non-Phys
Operator Theory for Analysis of Convex Optimization Methods in Machine Learning(机器学习凸优化方法分析的算子理论) 热度: Non-convexOptimizationforMachine Learning:Design,Analysis,and Understanding TengyuMa ADissertation PresentedtotheFaculty ofPrincetonUniversity ...
In the first part of this thesis we present novel results on optimization in infinite dimensional spaces and on the convergence of gradient descent when training overparameterized neural networks. In the second part of this thesis we discuss signal recovery methods with generative neural network ...
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solutions for Nonconvex Distributed Optimization By Mingyi Hong, Jason D. Lee, Meisam Razaviyayn 2018, Zhang-Aragam-Ravikumar-Xing DAGs with NO TEARS: Smooth Optimization for Structure LearningBy Xun Zheng, Bryon Aragam, Pradeep Rav...
Non-convex Optimization for Machine Learning (2017) 具有隐凸性或解析解的问题 These slides summarize lots of them. Blind Deconvolution using Convex Programming (2012) Intersecting Faces: Non-negative Matrix Factorization With New Guarantees (2015) The why and how of nonnegative matrix factorization (20...
is convex. thus, proving the acceleration becomes a harder task than the analysis for convex programming. 1.1 related work recently, there is a trend to study the nonconvex problem ( 2 ) in the machine learning and optimization community. recent developments come from two aspects: (1). the ...
Lu, and H. Liu, "Nonconvex statistical optimization: minimax-optimal sparse pca in polynomial time," arXiv preprint arXiv:1408.5352, 2014.Zhaoran Wang, Huanran Lu, and Han Liu, "Nonconvex statistical optimization: Minimax-optimal sparse pca in polynomial time," arXiv preprint arXiv:1408.5352...
Keywords optimization theory computational geometry equilibrium problems artificial intelligence algorithms machine learning discrete mathematics convex analysis Editors and Affiliations Department of Industrial and Systems Engineering, University of Florida, Gainesville, USA Panos M. Pardalos Mathematics...
Explore related subjects Discover the latest articles and news from researchers in related subjects, suggested using machine learning. Continuous Optimization Learning algorithms Machine Learning Non-parametric Inference Stochastic Learning and Adaptive Control Calculus of Variations and Optimization ...
However, the resultant optimization problem is much more challenging. A very recent state-of-the-art is based on the proximal gradient algorithm. However, it requires an expensive full SVD in each proximal step. In this paper, we show that for many commonly-used nonconvex low-rank regularizer...