Search Results for author: Siwei Wang

Found 26 papers, 7 papers with code

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.

Anomaly Detection Contrastive Learning

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 $O(\ln T)$.

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

Risk-Sensitive Reinforcement Learning: Iterated CVaR and the Worst Path

no code implementations6 Jun 2022 Yihan Du, Siwei Wang, Longbo Huang

In this paper, we study a novel episodic risk-sensitive Reinforcement Learning (RL) problem, named Iterated CVaR RL, where the objective is to maximize the tail of the reward-to-go at each step.

Autonomous Driving online learning +1

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.

Graph Clustering

Highly-Efficient Incomplete Large-Scale Multi-View Clustering With Consensus Bipartite Graph

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.

Incomplete multi-view 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 +3

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.

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

Cannot find the paper you are looking for? You can Submit a new open access paper.