536 citations · 1.3k across the 27 of their papers we have counts for
8 papers · 1 filter
Meta-Learning with Network Pruning
Hongduan Tian, Bo Liu, Xiao-Tong Yuan +1
Meta-learning is a powerful paradigm for few-shot learning. Although with remarkable success witnessed in many applications, the existing optimization based meta-learning models wi…
Dual Iterative Hard Thresholding: From Non-convex Sparse Minimization to Non-smooth Concave Maximization
Bo Liu, Xiao-Tong Yuan, Lezi Wang +2
Iterative Hard Thresholding (IHT) is a class of projected gradient descent methods for optimizing sparsity-constrained minimization models, with the best known efficiency and scala…
A Self-Paced Regularization Framework for Multi-Label Learning
Changsheng Li, Fan Wei, Junchi Yan +4
In this paper, we propose a novel multi-label learning framework, called Multi-Label Self-Paced Learning (MLSPL), in an attempt to incorporate the self-paced learning strategy into…
Self-Paced Multi-Task Learning
Changsheng Li, Junchi Yan, Fan Wei +3
In this paper, we propose a novel multi-task learning (MTL) framework, called Self-Paced Multi-Task Learning (SPMTL). Different from previous works treating all tasks and instances…
Graph Regularized Low Rank Representation for Aerosol Optical Depth Retrieval
Yubao Sun, Renlong Hang, Qingshan Liu +2
In this paper, we propose a novel data-driven regression model for aerosol optical depth (AOD) retrieval. First, we adopt a low rank representation (LRR) model to learn a powerful…
Joint Active Learning with Feature Selection via CUR Matrix Decomposition
Changsheng Li, Xiangfeng Wang, Weishan Dong +3
This paper presents an unsupervised learning approach for simultaneous sample and feature selection, which is in contrast to existing works which mainly tackle these two problems s…
Dynamic Structure Embedded Online Multiple-Output Regression for Stream Data
Changsheng Li, Fan Wei, Weishan Dong +3
Online multiple-output regression is an important machine learning technique for modeling, predicting, and compressing multi-dimensional correlated data streams. In this paper, we…
Max-Margin based Discriminative Feature Learning
Changsheng Li, Qingshan Liu, Weishan Dong +2
In this paper, we propose a new max-margin based discriminative feature learning method. Specifically, we aim at learning a low-dimensional feature representation, so as to maximiz…