Search Results for author: Siwei Wang

Found 52 papers, 18 papers with code

Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation

no code implementations28 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

Single-cell Multi-view Clustering via Community Detection with Unknown Number of Clusters

1 code implementation28 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.

Clustering Community Detection

Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent

1 code implementation11 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.

Clustering Multi-view Subspace Clustering

Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning

1 code implementation12 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.

Contrastive Learning Graph Anomaly Detection

Asymmetric double-winged multi-view clustering network for exploring Diverse and Consistent Information

no code implementations1 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.

Clustering Pseudo Label

Scalable Incomplete Multi-View Clustering with Structure Alignment

1 code implementation31 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).

Clustering graph construction +2

Efficient Multi-View Graph Clustering with Local and Global Structure Preservation

1 code implementation31 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.

Clustering Graph Clustering +1

DealMVC: Dual Contrastive Calibration for Multi-view Clustering

1 code implementation17 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.

Clustering Pseudo Label

CONVERT:Contrastive Graph Clustering with Reliable Augmentation

2 code implementations17 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).

Clustering Contrastive Learning +4

Unpaired Multi-View Graph Clustering with Cross-View Structure Matching

1 code implementation7 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.

Clustering Graph Clustering

Taming the Exponential Action Set: Sublinear Regret and Fast Convergence to Nash Equilibrium in Online Congestion Games

no code implementations19 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.

One-step Multi-view Clustering with Diverse Representation

no code implementations8 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.


Message Intercommunication for Inductive Relation Reasoning

no code implementations23 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.

Knowledge Graphs Relation

Quantifying stimulus-relevant representational drift using cross-modality contrastive learning

no code implementations19 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.

Contrastive Learning

Towards understanding neural collapse in supervised contrastive learning with the information bottleneck method

no code implementations19 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.

Contrastive Learning

Deep Temporal Graph Clustering

1 code implementation18 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.

Clustering Deep Clustering +3

A Game-theoretic Framework for Privacy-preserving Federated Learning

no code implementations11 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.

Federated Learning Privacy Preserving

Contextual Combinatorial Bandits with Probabilistically Triggered Arms

no code implementations30 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.

Auto-weighted Multi-view Clustering for Large-scale Data

1 code implementation21 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.


Cluster-guided Contrastive Graph Clustering Network

1 code implementation3 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.

Clustering Contrastive Learning +1

Cross-view Topology Based Consistent and Complementary Information for Deep Multi-view Clustering

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.

Clustering Deep Clustering +2

A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal

1 code implementation12 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.

General Knowledge Knowledge Graph Embedding +3

Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View

no code implementations1 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.

Contrastive Learning Graph Anomaly Detection

Dueling Bandits: From Two-dueling to Multi-dueling

no code implementations16 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 $O(\ln T)$.

Vocal Bursts Valence Prediction

Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms

no code implementations31 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.

Regret Analysis for Hierarchical Experts Bandit Problem

no code implementations11 Aug 2022 Qihan Guo, Siwei Wang, Jun Zhu

We study an extension of standard bandit problem in which there are R layers of experts.

Late Fusion Multi-view Clustering via Global and Local Alignment Maximization

1 code implementation2 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.


Multiple Kernel Clustering with Dual Noise Minimization

no code implementations13 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.


Local Sample-weighted Multiple Kernel Clustering with Consensus Discriminative Graph

1 code implementation5 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.


Thompson Sampling for (Combinatorial) Pure Exploration

no code implementations18 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.

Thompson Sampling

Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences

1 code implementation30 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.

Clustering Graph Clustering

Pure Exploration Bandit Problem with General Reward Functions Depending on Full Distributions

no code implementations8 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.

Multi-view Clustering with Deep Matrix Factorization and Global Graph Refinement

no code implementations1 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.


Continuous Mean-Covariance Bandits

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.

Decision Making

Multi-object Tracking with a Hierarchical Single-branch Network

no code implementations6 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.

Multi-Object Tracking Multiple Object Tracking +4

Localized Simple Multiple Kernel K-Means

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.


A One-Size-Fits-All Solution to Conservative Bandit Problems

no code implementations14 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.

Multi-Armed Bandits

Adaptive Algorithms for Multi-armed Bandit with Composite and Anonymous Feedback

no code implementations13 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.

Restless-UCB, an Efficient and Low-complexity Algorithm for Online Restless Bandits

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.

Multi-View Spectral Clustering with High-Order Optimal Neighborhood Laplacian Matrix

no code implementations31 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.

Clustering Vocal Bursts Intensity Prediction

CBNet: A Novel Composite Backbone Network Architecture for Object Detection

6 code implementations9 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.

Instance Segmentation object-detection +2

Multi-armed Bandits with Compensation

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.

Multi-Armed Bandits

Thompson Sampling for Combinatorial Semi-Bandits

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.

Thompson Sampling

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