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Contextual Combinatorial Bandits with Probabilistically Triggered Arms
Xutong Liu, Jinhang Zuo, Siwei Wang +4
We study contextual combinatorial bandits with probabilistically triggered arms (CMAB-T) under a variety of smoothness conditions that capture a wide range of applications, suc…
Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms
Xutong Liu, Jinhang Zuo, Siwei Wang +3
In this paper, we study the combinatorial semi-bandits (CMAB) and focus on reducing the dependency of the batch-size in the regret bound, where is the total number of arms…
GraphLearner: Graph Node Clustering with Fully Learnable Augmentation
Xihong Yang, Erxue Min, Ke Liang +6
Contrastive deep graph clustering (CDGC) leverages the power of contrastive learning to group nodes into different clusters. The quality of contrastive samples is crucial for achie…
Towards understanding neural collapse in supervised contrastive learning with the information bottleneck method
Siwei Wang, Stephanie E Palmer
Neural collapse describes the geometry of activation in the final layer of a deep neural network when it is trained beyond performance plateaus. Open questions include whether neur…
Deep Temporal Graph Clustering
Meng Liu, Yue Liu, Ke Liang +4
Deep graph clustering has recently received significant attention due to its ability to enhance the representation learning capabilities of models in unsupervised scenarios. Nevert…