58 citations · 158 across the 22 of their papers we have counts for
22 papers
Accelerated Cyclic Coordinate Dual Averaging with Extrapolation for Composite Convex Optimization
Cheuk Yin Lin, Chaobing Song, Jelena Diakonikolas
Exploiting partial first-order information in a cyclic way is arguably the most natural strategy to obtain scalable first-order methods. However, despite their wide use in practice…
Cyclic Block Coordinate Descent With Variance Reduction for Composite Nonconvex Optimization
Xufeng Cai, Chaobing Song, Stephen J. Wright +1
Nonconvex optimization is central in solving many machine learning problems, in which block-wise structure is commonly encountered. In this work, we propose cyclic block coordinate…
A Fast Scale-Invariant Algorithm for Non-negative Least Squares with Non-negative Data
Jelena Diakonikolas, Chenghui Li, Swati Padmanabhan +1
Nonnegative (linear) least square problems are a fundamental class of problems that is well-studied in statistical learning and for which solvers have been implemented in many of t…
Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone Inclusions
Xufeng Cai, Chaobing Song, Cristóbal Guzmán +1
We study stochastic monotone inclusion problems, which widely appear in machine learning applications, including robust regression and adversarial learning. We propose novel varian…
Coordinate Linear Variance Reduction for Generalized Linear Programming
Chaobing Song, Cheuk Yin Lin, Stephen J. Wright +1
We study a class of generalized linear programs (GLP) in a large-scale setting, which includes simple, possibly nonsmooth convex regularizer and simple convex set constraints. By r…
Optimistic Dual Extrapolation for Coherent Non-monotone Variational Inequalities
Chaobing Song, Zhengyuan Zhou, Yichao Zhou +2
The optimization problems associated with training generative adversarial neural networks can be largely reduced to certain {\em non-monotone} variational inequality problems (VIPs…
Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums
Chaobing Song, Stephen J. Wright, Jelena Diakonikolas
We study structured nonsmooth convex finite-sum optimization that appears widely in machine learning applications, including support vector machines and least absolute deviation. F…
Cyclic Coordinate Dual Averaging with Extrapolation
Chaobing Song, Jelena Diakonikolas
Cyclic block coordinate methods are a fundamental class of optimization methods widely used in practice and implemented as part of standard software packages for statistical learni…
Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction
Yaodong Yu, Kwan Ho Ryan Chan, Chong You +2
To learn intrinsic low-dimensional structures from high-dimensional data that most discriminate between classes, we propose the principle of Maximal Coding Rate Reduction ($\text{M…
Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization
Chaobing Song, Yong Jiang, Yi Ma
In this paper, we introduce a simplified and unified method for finite-sum convex optimization, named \emph{Variance Reduction via Accelerated Dual Averaging (VRADA)}. In both gene…
Breaking the Optimal Rate for a Class of Minimax Problems
Chaobing Song, Yong Jiang, Yi Ma
It is known that for convex optimization , the best possible rate of first order accelerated methods is . However, for th…
Unified Acceleration of High-Order Algorithms under Hölder Continuity and Uniform Convexity
Chaobing Song, Yong Jiang, Yi Ma
In this paper, through a very intuitive vanilla proximal method perspective, we derive accelerated high-order optimization algorithms for minimizing a convex function that has Höld…
Inexact Proximal Cubic Regularized Newton Methods for Convex Optimization
Chaobing Song, Ji Liu, Yong Jiang
In this paper, we use Proximal Cubic regularized Newton Methods (PCNM) to optimize the sum of a smooth convex function and a non-smooth convex function, where we use inexact gradie…
Fully Implicit Online Learning
Chaobing Song, Ji Liu, Han Liu +2
Regularized online learning is widely used in machine learning applications. In online learning, performing exact minimization ( implicit update) is known to be beneficial t…
Accelerated Stochastic Greedy Coordinate Descent by Soft Thresholding Projection onto Simplex
Chaobing Song, Shaobo Cui, Yong Jiang +1
In this paper we study the well-known greedy coordinate descent (GCD) algorithm to solve -regularized problems and improve GCD by the two popular strategies: Nesterov's acc…
Nonextensive information theoretical machine
Chaobing Song, Shu-Tao Xia
In this paper, we propose a new discriminative model named \emph{nonextensive information theoretical machine (NITM)} based on nonextensive generalization of Shannon information th…
Bayesian linear regression with Student-t assumptions
Chaobing Song, Shu-Tao Xia
As an automatic method of determining model complexity using the training data alone, Bayesian linear regression provides us a principled way to select hyperparameters. But one oft…
Alternating direction algorithms for regularization in compressed sensing
Chaobing Song, Shu-Tao Xia
In this paper we propose three iterative greedy algorithms for compressed sensing, called \emph{iterative alternating direction} (IAD), \emph{normalized iterative alternating direc…
Unifying Decision Trees Split Criteria Using Tsallis Entropy
Yisen Wang, Chaobing Song, Shu-Tao Xia
The construction of efficient and effective decision trees remains a key topic in machine learning because of their simplicity and flexibility. A lot of heuristic algorithms have b…
Subspace Thresholding Pursuit: A Reconstruction Algorithm for Compressed Sensing
Chao-Bing Song, Shu-Tao Xia, Xin-Ji Liu
We propose a new iterative greedy algorithm for reconstructions of sparse signals with or without noisy perturbations in compressed sensing. The proposed algorithm, called \emph{su…
Sparse signal recovery by minimization under restricted isometry property
Chao-Bing Song, Shu-Tao Xia
In the context of compressed sensing, the nonconvex minimization with has been studied in recent years. In this paper, by generalizing the sharp bound for …
Improved Analyses for SP and CoSaMP Algorithms in Terms of Restricted Isometry Constants
Chao-Bing Song, Shu-Tao Xia, Xin-ji Liu
In the context of compressed sensing (CS), both Subspace Pursuit (SP) and Compressive Sampling Matching Pursuit (CoSaMP) are very important iterative greedy recovery algorithms whi…