no code implementations • 16 Jan 2024 • Qixin Zhang, Zongqi Wan, Zengde Deng, Zaiyi Chen, Xiaoming Sun, Jialin Zhang, Yu Yang
The fundamental idea of our boosting technique is to exploit non-oblivious search to derive a novel auxiliary function $F$, whose stationary points are excellent approximations to the global maximum of the original DR-submodular objective $f$.
no code implementations • 21 May 2023 • Yue Xu, Qijie Shen, Jianwen Yin, Zengde Deng, Dimin Wang, Hao Chen, Lixiang Lai, Tao Zhuang, Junfeng Ge
Integrated recommendation, which aims at jointly recommending heterogeneous items from different channels in a main feed, has been widely applied to various online platforms.
no code implementations • 31 Mar 2023 • Jinxin Wang, Jiang Hu, Shixiang Chen, Zengde Deng, Anthony Man-Cho So
We focus on a class of non-smooth optimization problems over the Stiefel manifold in the decentralized setting, where a connected network of $n$ agents cooperatively minimize a finite-sum objective function with each component being weakly convex in the ambient Euclidean space.
no code implementations • 6 Mar 2023 • Yuwei Chen, Zengde Deng, Yinzhi Zhou, Zaiyi Chen, Yujie Chen, Haoyuan Hu
This paper studies the online stochastic resource allocation problem (RAP) with chance constraints.
no code implementations • 25 Sep 2022 • Yue Xu, Hao Chen, Zengde Deng, Yuanchen Bei, Feiran Huang
Third, we propose a layer ensemble technique which improves the expressiveness of the learned representations by assembling the layer-wise neighborhood representations at the final layer.
no code implementations • 18 Aug 2022 • Qixin Zhang, Zengde Deng, Xiangru Jian, Zaiyi Chen, Haoyuan Hu, Yu Yang
Maximizing a monotone submodular function is a fundamental task in machine learning, economics, and statistics.
no code implementations • 16 Aug 2022 • Qixin Zhang, Zengde Deng, Zaiyi Chen, Kuangqi Zhou, Haoyuan Hu, Yu Yang
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 domain of machine learning, economics, and operations research.
no code implementations • 30 Mar 2022 • Hao Chen, Zhong Huang, Yue Xu, Zengde Deng, Feiran Huang, Peng He, Zhoujun Li
The experimental results verify that our proposed NEGCN framework can significantly enhance the performance for various typical GCN models on both node classification and recommendation tasks.
no code implementations • 3 Jan 2022 • Qixin Zhang, Zengde Deng, Zaiyi Chen, Haoyuan Hu, Yu Yang
In the online setting, for the first time we consider the adversarial delays for stochastic gradient feedback, under which we propose a boosting online gradient algorithm with the same non-oblivious function $F$.
no code implementations • 9 May 2021 • Hao Chen, Zengde Deng, Yue Xu, Zhoujun Li
In this way, each node can be directly represented by concatenating the information extracted independently from each hop of its neighbors thereby avoiding the recursive neighborhood expansion across layers.
no code implementations • 29 Aug 2020 • Jinxin Wang, Zengde Deng, Taoli Zheng, Anthony Man-Cho So
Optimization for the MVSKC model is of great difficulty in two parts.
no code implementations • 7 Jun 2020 • Yue Xu, Hao Chen, Zengde Deng, Junxiong Zhu, Yanghua Li, Peng He, Wenyao Gao, Wenjun Xu
The results verify that the proposed model outperforms existing GCN models considerably and yields up to a few orders of magnitude speedup in training, in terms of the recommendation performance.
no code implementations • 5 May 2020 • Shixiang Chen, Zengde Deng, Shiqian Ma, Anthony Man-Cho So
Second, we propose a stochastic variant of ManPPA called StManPPA, which is well suited for large-scale computation, and establish its sublinear convergence rate.
1 code implementation • 12 Nov 2019 • Xiao Li, Shixiang Chen, Zengde Deng, Qing Qu, Zhihui Zhu, Anthony Man Cho So
To the best of our knowledge, these are the first convergence guarantees for using Riemannian subgradient-type methods to optimize a class of nonconvex nonsmooth functions over the Stiefel manifold.
no code implementations • 10 Jul 2019 • Hao Chen, Yue Xu, Feiran Huang, Zengde Deng, Wenbing Huang, Senzhang Wang, Peng He, Zhoujun Li
In this paper, we consider the problem of node classification and propose the Label-Aware Graph Convolutional Network (LAGCN) framework which can directly identify valuable neighbors to enhance the performance of existing GCN models.
no code implementations • 2 Jul 2019 • Yue Xu, Zengde Deng, Mengdi Wang, Wenjun Xu, Anthony Man-Cho So, Shuguang Cui
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 challenging but more useful in large-scale IoT.
no code implementations • 12 Mar 2019 • Zengde Deng, Anthony Man-Cho So
Variable selection is one of the most important tasks in statistics and machine learning.