60 citations · 541 across the 61 of their papers we have counts for
13 papers · 1 filter
Safe Sample Screening for Robust Support Vector Machine
Zhou Zhai, Bin Gu, Xiang Li +1
Robust support vector machine (RSVM) has been shown to perform remarkably well to improve the generalization performance of support vector machine under the noisy environment. Unfo…
Quadruply Stochastic Gradient Method for Large Scale Nonlinear Semi-Supervised Ordinal Regression AUC Optimization
Wanli Shi, Bin Gu, Xinag Li +1
Semi-supervised ordinal regression (SOR) problems are ubiquitous in real-world applications, where only a few ordered instances are labeled and massive instances remain unlabel…
Straggler-Agnostic and Communication-Efficient Distributed Primal-Dual Algorithm for High-Dimensional Data Mining
Zhouyuan Huo, Heng Huang
Recently, reducing communication time between machines becomes the main focus of distributed data mining. Previous methods propose to make workers do more computation locally befor…
Ouroboros: On Accelerating Training of Transformer-Based Language Models
Qian Yang, Zhouyuan Huo, Wenlin Wang +2
Language models are essential for natural language processing (NLP) tasks, such as machine translation and text summarization. Remarkable performance has been demonstrated recently…
Approaching Machine Learning Fairness through Adversarial Network
Xiaoqian Wang, Heng Huang
Fairness is becoming a rising concern w.r.t. machine learning model performance. Especially for sensitive fields such as criminal justice and loan decision, eliminating the predict…
On the Acceleration of Deep Learning Model Parallelism with Staleness
An Xu, Zhouyuan Huo, Heng Huang
Training the deep convolutional neural network for computer vision problems is slow and inefficient, especially when it is large and distributed across multiple devices. The ineffi…
Quadruply Stochastic Gradients for Large Scale Nonlinear Semi-Supervised AUC Optimization
Wanli Shi, Bin Gu, Xiang Li +2
Semi-supervised learning is pervasive in real-world applications, where only a few labeled data are available and large amounts of instances remain unlabeled. Since AUC is an impor…
Scalable Semi-Supervised SVM via Triply Stochastic Gradients
Xiang Geng, Bin Gu, Xiang Li +3
Semi-supervised learning (SSL) plays an increasingly important role in the big data era because a large number of unlabeled samples can be used effectively to improve the performan…
An Iteratively Re-weighted Method for Problems with Sparsity-Inducing Norms
Feiping Nie, Zhanxuan Hu, Xiaoqian Wang +3
This work aims at solving the problems with intractable sparsity-inducing norms that are often encountered in various machine learning tasks, such as multi-task learning, subspace…
Nonconvex Zeroth-Order Stochastic ADMM Methods with Lower Function Query Complexity
Feihu Huang, Shangqian Gao, Jian Pei +1
Zeroth-order (a.k.a, derivative-free) methods are a class of effective optimization methods for solving complex machine learning problems, where gradients of the objective function…
Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization
Feihu Huang, Shangqian Gao, Songcan Chen +1
Alternating direction method of multipliers (ADMM) is a popular optimization tool for the composite and constrained problems in machine learning. However, in many machine learning…
Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering
Chenyou Fan, Xiaofan Zhang, Shu Zhang +3
In this paper, we propose a novel end-to-end trainable Video Question Answering (VideoQA) framework with three major components: 1) a new heterogeneous memory which can effectively…
Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth Optimization
Feihu Huang, Bin Gu, Zhouyuan Huo +2
Proximal gradient method has been playing an important role to solve many machine learning tasks, especially for the nonsmooth problems. However, in some machine learning problems…