no code implementations • 21 Apr 2024 • Jiaxin Zhang, Yiqi Wang, Xihong Yang, Siwei Wang, Yu Feng, Yu Shi, Ruicaho Ren, En Zhu, Xinwang Liu
Graph Neural Networks have demonstrated great success in various fields of multimedia.
no code implementations • 11 Mar 2024 • Linyi Li, Shijie Geng, Zhenwen Li, Yibo He, Hao Yu, Ziyue Hua, Guanghan Ning, Siwei Wang, Tao Xie, Hongxia Yang
Large Language Models for understanding and generating code (code LLMs) have witnessed tremendous progress in recent years.
no code implementations • 28 Feb 2024 • Yu Chen, Xiangcheng Zhang, Siwei Wang, Longbo Huang
In this paper, we introduce a general framework on Risk-Sensitive Distributional Reinforcement Learning (RS-DisRL), with static Lipschitz Risk Measures (LRM) and general function approximation.
Distributional Reinforcement Learning reinforcement-learning +1
1 code implementation • 28 Nov 2023 • Dayu Hu, Zhibin Dong, Ke Liang, Jun Wang, Siwei Wang, Xinwang Liu
To this end, we introduce scUNC, an innovative multi-view clustering approach tailored for single-cell data, which seamlessly integrates information from different views without the need for a predefined number of clusters.
1 code implementation • 11 Oct 2023 • Qiyuan Ou, Siwei Wang, Pei Zhang, Sihang Zhou, En Zhu
However, we propose Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent(MVSC-HFD) to tackle the discrepancy among views through hierarchical feature descent and project to a common subspace( STAGE 1), which reveals dependency of different views.
1 code implementation • 12 Sep 2023 • Jingcan Duan, Pei Zhang, Siwei Wang, Jingtao Hu, Hu Jin, Jiaxin Zhang, Haifang Zhou, Xinwang Liu
Finally, the model is refined with the only input of reliable normal nodes and learns a more accurate estimate of normality so that anomalous nodes can be more easily distinguished.
no code implementations • 1 Sep 2023 • Qun Zheng, Xihong Yang, Siwei Wang, Xinru An, Qi Liu
In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC) is becoming a hot research spot, which aims to mine the potential relationships between different views.
1 code implementation • 31 Aug 2023 • Yi Wen, Siwei Wang, Ke Liang, Weixuan Liang, Xinhang Wan, Xinwang Liu, Suyuan Liu, Jiyuan Liu, En Zhu
Although several anchor-based IMVC methods have been proposed to process the large-scale incomplete data, they still suffer from the following drawbacks: i) Most existing approaches neglect the inter-view discrepancy and enforce cross-view representation to be consistent, which would corrupt the representation capability of the model; ii) Due to the samples disparity between different views, the learned anchor might be misaligned, which we referred as the Anchor-Unaligned Problem for Incomplete data (AUP-ID).
1 code implementation • 31 Aug 2023 • Yi Wen, Suyuan Liu, Xinhang Wan, Siwei Wang, Ke Liang, Xinwang Liu, Xihong Yang, Pei Zhang
Anchor-based multi-view graph clustering (AMVGC) has received abundant attention owing to its high efficiency and the capability to capture complementary structural information across multiple views.
1 code implementation • 17 Aug 2023 • Xihong Yang, Jiaqi Jin, Siwei Wang, Ke Liang, Yue Liu, Yi Wen, Suyuan Liu, Sihang Zhou, Xinwang Liu, En Zhu
Then, a global contrastive calibration loss is proposed by aligning the view feature similarity graph and the high-confidence pseudo-label graph.
2 code implementations • 17 Aug 2023 • Xihong Yang, Cheng Tan, Yue Liu, Ke Liang, Siwei Wang, Sihang Zhou, Jun Xia, Stan Z. Li, Xinwang Liu, En Zhu
To address these problems, we propose a novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (CONVERT).
1 code implementation • 7 Jul 2023 • Yi Wen, Siwei Wang, Qing Liao, Weixuan Liang, Ke Liang, Xinhang Wan, Xinwang Liu
Besides, our UPMGC-SM is a unified framework for both the fully and partially unpaired multi-view graph clustering.
no code implementations • 6 Jul 2023 • Yu Chen, Yihan Du, Pihe Hu, Siwei Wang, Desheng Wu, Longbo Huang
Risk-sensitive reinforcement learning (RL) aims to optimize policies that balance the expected reward and risk.
no code implementations • 19 Jun 2023 • Jing Dong, Jingyu Wu, Siwei Wang, Baoxiang Wang, Wei Chen
The congestion game is a powerful model that encompasses a range of engineering systems such as traffic networks and resource allocation.
no code implementations • 8 Jun 2023 • Xinhang Wan, Jiyuan Liu, Xinwang Liu, Siwei Wang, Yi Wen, Tianjiao Wan, Li Shen, En Zhu
In light of this, we propose a one-step multi-view clustering with diverse representation method, which incorporates multi-view learning and $k$-means into a unified framework.
no code implementations • 8 Jun 2023 • Meng Liu, Ke Liang, Yue Liu, Siwei Wang, Sihang Zhou, Xinwang Liu
It makes evaluating models for large-scale temporal graph clustering challenging.
no code implementations • 23 May 2023 • Ke Liang, Lingyuan Meng, Sihang Zhou, Siwei Wang, Wenxuan Tu, Yue Liu, Meng Liu, Xinwang Liu
However, the uni-directional message-passing mechanism hinders such models from exploiting hidden mutual relations between entities in directed graphs.
no code implementations • 19 May 2023 • Siwei Wang, Elizabeth A de Laittre, Jason MacLean, Stephanie E Palmer
The representational drift observed in neural populations raises serious questions about how accurate decoding survives these changes.
no code implementations • 19 May 2023 • Siwei Wang, Stephanie E Palmer
We demonstrate that neural collapse leads to good generalization specifically when it approaches an optimal IB solution of the classification problem.
1 code implementation • 18 May 2023 • Meng Liu, Yue Liu, Ke Liang, Wenxuan Tu, Siwei Wang, Sihang Zhou, Xinwang Liu
To solve the problem, we propose a general framework for deep Temporal Graph Clustering called TGC, which introduces deep clustering techniques to suit the interaction sequence-based batch-processing pattern of temporal graphs.
no code implementations • 11 Apr 2023 • Xiaojin Zhang, Lixin Fan, Siwei Wang, Wenjie Li, Kai Chen, Qiang Yang
To address this, we propose the first game-theoretic framework that considers both FL defenders and attackers in terms of their respective payoffs, which include computational costs, FL model utilities, and privacy leakage risks.
no code implementations • 30 Mar 2023 • Xutong Liu, Jinhang Zuo, Siwei Wang, John C. S. Lui, Mohammad Hajiesmaili, Adam Wierman, Wei Chen
We study contextual combinatorial bandits with probabilistically triggered arms (C$^2$MAB-T) under a variety of smoothness conditions that capture a wide range of applications, such as contextual cascading bandits and contextual influence maximization bandits.
no code implementations • CVPR 2023 • Jiaqi Jin, Siwei Wang, Zhibin Dong, Xinwang Liu, En Zhu
The success of existing multi-view clustering relies on the assumption of sample integrity across multiple views.
1 code implementation • 21 Jan 2023 • Xinhang Wan, Xinwang Liu, Jiyuan Liu, Siwei Wang, Yi Wen, Weixuan Liang, En Zhu, Zhe Liu, Lu Zhou
Multi-view clustering has gained broad attention owing to its capacity to exploit complementary information across multiple data views.
1 code implementation • 3 Jan 2023 • Xihong Yang, Yue Liu, Sihang Zhou, Siwei Wang, Wenxuan Tu, Qun Zheng, Xinwang Liu, Liming Fang, En Zhu
Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views.
no code implementations • ICCV 2023 • Zhibin Dong, Siwei Wang, Jiaqi Jin, Xinwang Liu, En Zhu
However, most existing deep clustering approaches are dedicated to merging and exploring the consistent latent representation across multiple views while overlooking the abundant complementary information in each view.
1 code implementation • 12 Dec 2022 • Ke Liang, Lingyuan Meng, Meng Liu, Yue Liu, Wenxuan Tu, Siwei Wang, Sihang Zhou, Xinwang Liu, Fuchun Sun
According to the graph types, existing KGR models can be roughly divided into three categories, i. e., static models, temporal models, and multi-modal models.
no code implementations • 1 Dec 2022 • Jingcan Duan, Siwei Wang, Pei Zhang, En Zhu, Jingtao Hu, Hu Jin, Yue Liu, Zhibin Dong
However, they neglect the subgraph-subgraph comparison information which the normal and abnormal subgraph pairs behave differently in terms of embeddings and structures in GAD, resulting in sub-optimal task performance.
no code implementations • 28 Nov 2022 • Jingcan Duan, Bin Xiao, Siwei Wang, Haifang Zhou, Xinwang Liu
The average node-pair similarity can be regarded as the topology anomaly degree of nodes within substructures.
no code implementations • 16 Nov 2022 • Yihan Du, Siwei Wang, Longbo Huang
DoublerBAI provides a generic schema for translating known results on best arm identification algorithms to the dueling bandit problem, and achieves a regret bound of $O(\ln T)$.
no code implementations • 31 Aug 2022 • Xutong Liu, Jinhang Zuo, Siwei Wang, Carlee Joe-Wong, John C. S. Lui, Wei Chen
Under this new condition, we propose a BCUCB-T algorithm with variance-aware confidence intervals and conduct regret analysis which reduces the $O(K)$ factor to $O(\log K)$ or $O(\log^2 K)$ in the regret bound, significantly improving the regret bounds for the above applications.
no code implementations • 11 Aug 2022 • Qihan Guo, Siwei Wang, Jun Zhu
We study an extension of standard bandit problem in which there are R layers of experts.
1 code implementation • 2 Aug 2022 • Siwei Wang, Xinwang Liu, En Zhu
It optimally fuses multiple source information in partition level from each individual view, and maximally aligns the consensus partition with these weighted base ones.
no code implementations • 13 Jul 2022 • Junpu Zhang, Liang Li, Siwei Wang, Jiyuan Liu, Yue Liu, Xinwang Liu, En Zhu
As a representative, late fusion MKC first decomposes the kernels into orthogonal partition matrices, then learns a consensus one from them, achieving promising performance recently.
1 code implementation • 5 Jul 2022 • Liang Li, Siwei Wang, Xinwang Liu, En Zhu, Li Shen, Kenli Li, Keqin Li
Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels.
no code implementations • 18 Jun 2022 • Siwei Wang, Jun Zhu
To make the algorithm efficient, they usually use the sum of upper confidence bounds within arm set $S$ to represent the upper confidence bound of $S$, which can be much larger than the tight upper confidence bound of $S$ and leads to a much higher complexity than necessary, since the empirical means of different arms in $S$ are independent.
no code implementations • 6 Jun 2022 • Yihan Du, Siwei Wang, Longbo Huang
For Worst Path RL, we propose an efficient algorithm with constant upper and lower bounds.
1 code implementation • 30 May 2022 • Siwei Wang, Xinwang Liu, Suyuan Liu, Jiaqi Jin, Wenxuan Tu, Xinzhong Zhu, En Zhu
Under multi-view scenarios, generating correct correspondences could be extremely difficult since anchors are not consistent in feature dimensions.
1 code implementation • CVPR 2022 • Siwei Wang, Xinwang Liu, Li Liu, Wenxuan Tu, Xinzhong Zhu, Jiyuan Liu, Sihang Zhou, En Zhu
Multi-view clustering has received increasing attention due to its effectiveness in fusing complementary information without manual annotations.
no code implementations • 8 May 2021 • Siwei Wang, Wei Chen
In this paper, we study the pure exploration bandit model on general distribution functions, which means that the reward function of each arm depends on the whole distribution, not only its mean.
no code implementations • 1 May 2021 • Chen Zhang, Siwei Wang, Wenxuan Tu, Pei Zhang, Xinwang Liu, Changwang Zhang, Bo Yuan
Multi-view clustering is an important yet challenging task in machine learning and data mining community.
no code implementations • 1 May 2021 • Chen Zhang, Siwei Wang, Jiyuan Liu, Sihang Zhou, Pei Zhang, Xinwang Liu, En Zhu, Changwang Zhang
iii) The partition level information has not been utilized in existing work.
no code implementations • 21 Mar 2021 • Mingjie Luo, Siwei Wang, Xinwang Liu, Wenxuan Tu, Yi Zhang, Xifeng Guo, Sihang Zhou, En Zhu
Clustering is a fundamental task in the computer vision and machine learning community.
no code implementations • NeurIPS 2021 • Yihan Du, Siwei Wang, Zhixuan Fang, Longbo Huang
To the best of our knowledge, this is the first work that considers option correlation in risk-aware bandits and explicitly quantifies how arbitrary covariance structures impact the learning performance.
no code implementations • 6 Jan 2021 • Fan Wang, Lei Luo, En Zhu, Siwei Wang, Jun Long
Recent Multiple Object Tracking (MOT) methods have gradually attempted to integrate object detection and instance re-identification (Re-ID) into a united network to form a one-stage solution.
1 code implementation • ICCV 2021 • Xinwang Liu, Sihang Zhou, Li Liu, Chang Tang, Siwei Wang, Jiyuan Liu, Yi Zhang
After that, we theoretically show that the objective of SimpleMKKM is a special case of this local kernel alignment criterion with normalizing each base kernel matrix.
no code implementations • 14 Dec 2020 • Yihan Du, Siwei Wang, Longbo Huang
In this paper, we study a family of conservative bandit problems (CBPs) with sample-path reward constraints, i. e., the learner's reward performance must be at least as well as a given baseline at any time.
no code implementations • 13 Dec 2020 • Siwei Wang, Haoyun Wang, Longbo Huang
Existing results on this model require prior knowledge about the reward interval size as an input to their algorithms.
no code implementations • NeurIPS 2020 • Siwei Wang, Longbo Huang, John C. S. Lui
Compared to existing algorithms, our result eliminates the exponential factor (in $M, N$) in the regret upper bound, due to a novel exploitation of the sparsity in transitions in general restless bandit problems.
no code implementations • 31 Aug 2020 • Weixuan Liang, Sihang Zhou, Jian Xiong, Xinwang Liu, Siwei Wang, En Zhu, Zhiping Cai, Xin Xu
Multi-view spectral clustering can effectively reveal the intrinsic cluster structure among data by performing clustering on the learned optimal embedding across views.
6 code implementations • 9 Sep 2019 • Yudong Liu, Yongtao Wang, Siwei Wang, Ting-Ting Liang, Qijie Zhao, Zhi Tang, Haibin Ling
In existing CNN based detectors, the backbone network is a very important component for basic feature extraction, and the performance of the detectors highly depends on it.
Ranked #47 on Instance Segmentation on COCO test-dev
no code implementations • NeurIPS 2018 • Siwei Wang, Longbo Huang
We propose and study the known-compensation multi-arm bandit (KCMAB) problem, where a system controller offers a set of arms to many short-term players for $T$ steps.
no code implementations • ICML 2018 • Siwei Wang, Wei Chen
We first analyze the standard TS algorithm for the general CMAB model when the outcome distributions of all the base arms are independent, and obtain a distribution-dependent regret bound of $O(m\log K_{\max}\log T / \Delta_{\min})$, where $m$ is the number of base arms, $K_{\max}$ is the size of the largest super arm, $T$ is the time horizon, and $\Delta_{\min}$ is the minimum gap between the expected reward of the optimal solution and any non-optimal solution.