Large-scale machine learning and convex optimizationFrancis Bach
这本书是由 Michael Nielsen 和 Isaac Schreiber 合著的，于2019年由MIT出版社出版。该书是机器学习领域中关于非凸优化问题的经典著作之一，主要介绍了一些非凸优化算法以及如何求解非凸优化问题。书中主要讲了两种非凸情况：一是目标函数是凸的，约束集合不是凸的，即 f(x) 凸，C 非凸；二是目标函数不是凸的...
Parameter Learning: a Convex Analytic Path Abstract The goal of this chapter is to present an overview of techniques forconvex optimizationin the context of machine learning. It starts from the definitions ofconvex sets, functions, and the projection operator and some of its properties are derived...
Optimization is a big part of machine learning. It is the core of most popular methods, from least squares regression to artificial neural networks. In this post you will discover recipes for 5 optimization algorithms in R. These methods might be useful in the core of your own implementation ...
We study adaptive methods for differentially private convex optimization, proposing and analyzing differentially private variants of a…
Private Stochastic Convex Optimization: Optimal Rates in ℓ1 Geometry Stochastic convex optimization over an ℓ1ℓ_1ℓ1-bounded domain is ubiquitous in machine learning applications such as LASSO but remains poorly understood when learning with differential privacy. We show that, up to logar...
Optimization for Machine Learning 9.4 Introductory Lectures on Convex O... 9.4 The Elements of Statistical Learnin... 9.4 Probabilistic Graphical Models 9.2 Foundations of Machine Learning 8.9 Learning with Kernels 9.8 Pattern Recognition and Machine L... 9.5 Machine Learning 9.1 Pred...
Numerical Optimization9.2 Optimization for Machine Learning9.4 The Elements of Statistical Learnin...9.4 Learning with Kernels9.8 Machine Learning9.1 Probabilistic Graphical Models9.2 Foundations of Machine Learning8.9 Information Theory, Inference and ...9.3 ...
We investigate the problem of online convex optimization with unknown delays, in which the feedback of a decision arrives with an arbitrary delay. Previous studies have presented delayed online gradient descent (DOGD), and achieved the regret bound of O(D) by only utilizing the convexity conditio...
convex optimization, using cutting plane/ellipsoid/subgradientconvex optimization to machine learning, and significanceoptimization problems, linear/geometric programming/Lagrange Dualitydeterministic/stochastic algorithms in solving optimization problemsrobust optimization, for robust waveform diversity...