16 citations · 29 across the 16 of their papers we have counts for
18 papers
DreaMontage: Arbitrary Frame-Guided One-Shot Video Generation
Jiawei Liu, Junqiao Li, Jiangfan Deng +11
The "one-shot" technique represents a distinct and sophisticated aesthetic in filmmaking. However, its practical realization is often hindered by prohibitive costs and complex real…
Boosting Gradient Ascent for Continuous DR-submodular Maximization
Qixin Zhang, Zongqi Wan, Zengde Deng +4
Projected Gradient Ascent (PGA) is the most commonly used optimization scheme in machine learning and operations research areas. Nevertheless, numerous studies and examples have sh…
Multi-channel Integrated Recommendation with Exposure Constraints
Yue Xu, Qijie Shen, Jianwen Yin +6
Integrated recommendation, which aims at jointly recommending heterogeneous items from different channels in a main feed, has been widely applied to various online platforms. Thoug…
Decentralized Weakly Convex Optimization Over the Stiefel Manifold
Jinxin Wang, Jiang Hu, Shixiang Chen +2
We focus on a class of non-smooth optimization problems over the Stiefel manifold in the decentralized setting, where a connected network of agents cooperatively minimize a fin…
An Online Algorithm for Chance Constrained Resource Allocation
Yuwei Chen, Zengde Deng, Yinzhi Zhou +3
This paper studies the online stochastic resource allocation problem (RAP) with chance constraints. The online RAP is a 0-1 integer linear programming problem where the resource co…
Flattened Graph Convolutional Networks For Recommendation
Yue Xu, Hao Chen, Zengde Deng +2
Graph Convolutional Networks (GCNs) and their variants have achieved significant performances on various recommendation tasks. However, many existing GCN models tend to perform rec…
Communication-Efficient Decentralized Online Continuous DR-Submodular Maximization
Qixin Zhang, Zengde Deng, Xiangru Jian +3
Maximizing a monotone submodular function is a fundamental task in machine learning, economics, and statistics. In this paper, we present two communication-efficient decentralized…
Online Learning for Non-monotone Submodular Maximization: From Full Information to Bandit Feedback
Qixin Zhang, Zengde Deng, Zaiyi Chen +3
In this paper, we revisit the online non-monotone continuous DR-submodular maximization problem over a down-closed convex set, which finds wide real-world applications in the domai…
Online Primal-Dual Algorithms For Stochastic Resource Allocation Problems
Yuwei Chen, Zengde Deng, Zaiyi Chen +3
This paper studies the online stochastic resource allocation problem (RAP) with chance constraints and conditional expectation constraints. The online RAP is an integer linear prog…
Neighbor Enhanced Graph Convolutional Networks for Node Classification and Recommendation
Hao Chen, Zhong Huang, Yue Xu +4
The recently proposed Graph Convolutional Networks (GCNs) have achieved significantly superior performance on various graph-related tasks, such as node classification and recommend…
Stochastic Continuous Submodular Maximization: Boosting via Non-oblivious Function
Qixin Zhang, Zengde Deng, Zaiyi Chen +2
In this paper, we revisit Stochastic Continuous Submodular Maximization in both offline and online settings, which can benefit wide applications in machine learning and operations…
Non-Recursive Graph Convolutional Networks
Hao Chen, Zengde Deng, Yue Xu +1
Graph Convolutional Networks (GCNs) are powerful models for node representation learning tasks. However, the node representation in existing GCN models is usually generated by perf…
Sparse High-Order Portfolios via Proximal DCA and SCA
Jinxin Wang, Zengde Deng, Taoli Zheng +1
In this paper, we aim at solving the cardinality constrained high-order portfolio optimization, i.e., mean-variance-skewness-kurtosis model with cardinality constraint (MVSKC). Opt…
Single-Layer Graph Convolutional Networks For Recommendation
Yue Xu, Hao Chen, Zengde Deng +5
Graph Convolutional Networks (GCNs) and their variants have received significant attention and achieved start-of-the-art performances on various recommendation tasks. However, many…
Manifold Proximal Point Algorithms for Dual Principal Component Pursuit and Orthogonal Dictionary Learning
Shixiang Chen, Zengde Deng, Shiqian Ma +1
We consider the problem of maximizing the norm of a linear map over the sphere, which arises in various machine learning applications such as orthogonal dictionary learnin…
Weakly Convex Optimization over Stiefel Manifold Using Riemannian Subgradient-Type Methods
Xiao Li, Shixiang Chen, Zengde Deng +3
We consider a class of nonsmooth optimization problems over the Stiefel manifold, in which the objective function is weakly convex in the ambient Euclidean space. Such problems are…
Voting-Based Multi-Agent Reinforcement Learning for Intelligent IoT
Yue Xu, Zengde Deng, Mengdi Wang +3
The recent success of single-agent reinforcement learning (RL) in Internet of things (IoT) systems motivates the study of multi-agent reinforcement learning (MARL), which is more c…
An Efficient Augmented Lagrangian Based Method for Constrained Lasso
Zengde Deng, Anthony Man-Cho So
Variable selection is one of the most important tasks in statistics and machine learning. To incorporate more prior information about the regression coefficients, the constrained L…