English
Related papers

Related papers: Variance Reduction with Sparse Gradients

200 papers

Stein Variational Gradient Descent (SVGD) is a highly efficient method to sample from an unnormalized probability distribution. However, the SVGD update relies on gradients of the log-density, which may not always be available. Existing…

Machine Learning · Computer Science 2026-03-13 Cornelius V. Braun , Robert T. Lange , Marc Toussaint

Subsampling is a widely used and effective approach for addressing the computational challenges posed by massive datasets. Substantial progress has been made in developing non-uniform, probability-based subsampling schemes that prioritize…

Methodology · Statistics 2026-05-07 Dingyi Wang , Haiying Wang , Qingpei Hu

The Stochastic Extragradient (SEG) method is one of the most popular algorithms for solving min-max optimization and variational inequalities problems (VIP) appearing in various machine learning tasks. However, several important questions…

Optimization and Control · Mathematics 2022-02-23 Eduard Gorbunov , Hugo Berard , Gauthier Gidel , Nicolas Loizou

Recent advances in deep reinforcement learning have achieved human-level performance on a variety of real-world applications. However, the current algorithms still suffer from poor gradient estimation with excessive variance, resulting in…

Machine Learning · Computer Science 2019-05-21 Wei-Ye Zhao , Xi-Ya Guan , Yang Liu , Xiaoming Zhao , Jian Peng

With the vigorous development of artificial intelligence technology, various engineering technology applications have been implemented one after another. The gradient descent method plays an important role in solving various optimization…

Machine Learning · Computer Science 2021-04-27 Jinhuan Duan , Xianxian Li , Shiqi Gao , Jinyan Wang , Zili Zhong

The mini-batch stochastic gradient descent (SGD) algorithm is widely used in training machine learning models, in particular deep learning models. We study SGD dynamics under linear regression and two-layer linear networks, with an easy…

Optimization and Control · Mathematics 2020-04-29 Xin Qian , Diego Klabjan

Stochastic gradient descent (SGD) is a prevalent optimization technique for large-scale distributed machine learning. While SGD computation can be efficiently divided between multiple machines, communication typically becomes a bottleneck…

Machine Learning · Computer Science 2021-05-24 Dmitrii Avdiukhin , Grigory Yaroslavtsev

We consider convex-concave saddle-point problems where the objective functions may be split in many components, and extend recent stochastic variance reduction methods (such as SVRG or SAGA) to provide the first large-scale linearly…

Machine Learning · Computer Science 2016-11-04 P Balamurugan , Francis Bach

As an adaptive, interpretable, robust, and accurate meta-algorithm for arbitrary differentiable loss functions, gradient tree boosting is one of the most popular machine learning techniques, though the computational expensiveness severely…

Machine Learning · Computer Science 2019-11-21 Daniel Chao Zhou , Zhongming Jin , Tong Zhang

While nowadays most gradient-based optimization methods focus on exploring the high-dimensional geometric features, the random error accumulated in a stochastic version of any algorithm implementation has not been stressed yet. In this…

Machine Learning · Computer Science 2020-08-14 Tong Yang , Long Sha , Pengyu Hong

Despite an extensive body of literature on deep learning optimization, our current understanding of what makes an optimization algorithm effective is fragmented. In particular, we do not understand well whether enhanced optimization…

Machine Learning · Computer Science 2024-03-04 Toki Tahmid Inan , Mingrui Liu , Amarda Shehu

Large-scale non-convex sparsity-constrained problems have recently gained extensive attention. Most existing deterministic optimization methods (e.g., GraSP) are not suitable for large-scale and high-dimensional problems, and thus…

Machine Learning · Computer Science 2019-12-03 Fanhua Shang , Bingkun Wei , Hongying Liu , Yuanyuan Liu , Jiacheng Zhuo

Modern large scale machine learning applications require stochastic optimization algorithms to be implemented on distributed computational architectures. A key bottleneck is the communication overhead for exchanging information, such as…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-10-25 Zijie Yan

Stochastic Variance Reduced Gradient (SVRG), introduced by Johnson & Zhang (2013), is a theoretically compelling optimization method. However, as Defazio & Bottou (2019) highlight, its effectiveness in deep learning is yet to be proven. In…

Machine Learning · Computer Science 2025-03-18 Yida Yin , Zhiqiu Xu , Zhiyuan Li , Trevor Darrell , Zhuang Liu

In high-dimensional statistics, variable selection recovers the latent sparse patterns from all possible covariate combinations. This paper proposes a novel optimization method to solve the exact L0-regularized regression problem, which is…

Methodology · Statistics 2022-06-02 Mingzhang Yin , Nhat Ho , Bowei Yan , Xiaoning Qian , Mingyuan Zhou

Distributed training of massive machine learning models, in particular deep neural networks, via Stochastic Gradient Descent (SGD) is becoming commonplace. Several families of communication-reduction methods, such as quantization,…

Machine Learning · Computer Science 2018-09-28 Dan Alistarh , Torsten Hoefler , Mikael Johansson , Sarit Khirirat , Nikola Konstantinov , Cédric Renggli

We present a unified theorem for the convergence analysis of stochastic gradient algorithms for minimizing a smooth and convex loss plus a convex regularizer. We do this by extending the unified analysis of Gorbunov, Hanzely \& Richt\'arik…

Machine Learning · Computer Science 2020-06-23 Ahmed Khaled , Othmane Sebbouh , Nicolas Loizou , Robert M. Gower , Peter Richtárik

Asynchronous parallel optimization algorithms for solving large-scale machine learning problems have drawn significant attention from academia to industry recently. This paper proposes a novel algorithm, decoupled asynchronous proximal…

Optimization and Control · Mathematics 2016-05-24 Yitan Li , Linli Xu , Xiaowei Zhong , Qing Ling

In this paper, we revisit \textsf{ROOT-SGD}, an innovative method for stochastic optimization to bridge the gap between stochastic optimization and statistical efficiency. The proposed method enhances the performance and reliability of…

Machine Learning · Statistics 2024-08-26 Chris Junchi Li

Nonconvex-concave (NC-C) finite-sum minimax problems have wide applications in signal processing and machine learning tasks. Conventional stochastic gradient algorithms, which rely on uniform sampling for gradient estimation, often suffer…

Optimization and Control · Mathematics 2025-10-14 Xia Jiang , Linglingzhi Zhu , Taoli Zheng , Anthony Man-Cho So
‹ Prev 1 4 5 6 7 8 10 Next ›