Graph anomaly detection (GAD) has attracted increasing attention in mach...
In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC...
Anchor-based multi-view graph clustering (AMVGC) has received abundant
a...
The success of existing multi-view clustering (MVC) relies on the assump...
Benefiting from the strong view-consistent information mining capacity,
...
Multi-view clustering (MVC), which effectively fuses information from
mu...
Risk-sensitive reinforcement learning (RL) aims to optimize policies tha...
The congestion game is a powerful model that encompasses a range of
engi...
Multi-view clustering has attracted broad attention due to its capacity ...
Neural collapse describes the geometry of activation in the final layer ...
In federated learning, benign participants aim to optimize a global mode...
We study contextual combinatorial bandits with probabilistically trigger...
The success of existing multi-view clustering relies on the assumption o...
Benefiting from the intrinsic supervision information exploitation
capab...
Graph contrastive learning is an important method for deep graph cluster...
Graph anomaly detection (GAD) is a vital task in graph-based machine lea...
We study a general multi-dueling bandit problem, where an agent compares...
In this paper, we study the combinatorial semi-bandits (CMAB) and focus ...
We study an extension of standard bandit problem in which there are R la...
Multi-view clustering (MVC) optimally integrates complementary informati...
Clustering is a representative unsupervised method widely applied in
mul...
Multiple kernel clustering (MKC) is committed to achieving optimal
infor...
Existing methods of combinatorial pure exploration mainly focus on the U...
In this paper, we study a novel episodic risk-sensitive Reinforcement
Le...
Multi-view anchor graph clustering selects representative anchors to avo...
In this paper, we study the pure exploration bandit model on general
dis...
Multi-view clustering (MVC) has been extensively studied to collect mult...
Multi-view clustering is an important yet challenging task in machine
le...
Clustering is a fundamental task in the computer vision and machine lear...
Existing risk-aware multi-armed bandit models typically focus on risk
me...
Recent Multiple Object Tracking (MOT) methods have gradually attempted t...
In this paper, we study a family of conservative bandit problems (CBPs) ...
We study the multi-armed bandit (MAB) problem with composite and anonymo...
We study the online restless bandit problem, where the state of each arm...
Multi-view spectral clustering can effectively reveal the intrinsic clus...
In existing CNN based detectors, the backbone network is a very importan...
We propose and study the known-compensation multi-arm bandit (KCMAB) pro...
We study the application of the Thompson Sampling (TS) methodology to th...