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Semantic DLM+: Improving Diffusion Language Models through Bias-variance Trade-off in Transition Kernel Design
Keyue Jiang, Yuxiang Wang, Yanan Zhao +7
Diffusion Language Models (DLMs) have demonstrated strong scaling capacity as alternatives to autoregressive language models. However, their performance is highly sensitive to the…
GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models
Xiaohang Tang, Keyue Jiang, Che Liu +4
Reinforcement learning (RL) can be used to improve the policy (denoiser) of diffusion large language models (dLLMs), while being hindered by the intractability of the policy likeli…
On the Trainability of Masked Diffusion Language Models via Blockwise Locality
Yuxiang Wang, Yu Xiang, Baojian Zhou +4
Masked diffusion language models (MDMs) have recently emerged as a promising alternative to standard autoregressive large language models (AR-LLMs), yet their optimization can be s…
Impact of Connectivity on Laplacian Representations in Reinforcement Learning
Tommaso Giorgi, Pierriccardo Olivieri, Keyue Jiang +2
Learning compact state representations in Markov Decision Processes (MDPs) has proven crucial for addressing the curse of dimensionality in large-scale reinforcement learning (RL)…
LGDC: Latent Graph Diffusion via Spectrum-Preserving Coarsening
Nagham Osman, Keyue Jiang, Davide Buffelli +2
Graph generation is a critical task across scientific domains. Existing methods fall broadly into two categories: autoregressive models, which iteratively expand graphs, and one-sh…
Adaptive Multi-view Graph Contrastive Learning via Fractional-order Neural Diffusion Networks
Yanan Zhao, Feng Ji, Jingyang Dai +4
Graph contrastive learning (GCL) learns node and graph representations by contrasting multiple views of the same graph. Existing methods typically rely on fixed, handcrafted views-…
A Stage-Aware Mixture of Experts Framework for Neurodegenerative Disease Progression Modelling
Tiantian He, Keyue Jiang, An Zhao +5
The long-term progression of neurodegenerative diseases is commonly conceptualized as a spatiotemporal diffusion process that consists of a graph diffusion process across the struc…
Bures-Wasserstein Flow Matching for Graph Generation
Keyue Jiang, Jiahao Cui, Xiaowen Dong +1
Graph generation has emerged as a critical task in fields ranging from drug discovery to circuit design. Contemporary approaches, notably diffusion and flow-based models, have achi…
Heterogeneous Graph Structure Learning through the Lens of Data-generating Processes
Keyue Jiang, Bohan Tang, Xiaowen Dong +1
Inferring the graph structure from observed data is a key task in graph machine learning to capture the intrinsic relationship between data entities. While significant advancements…
Effects of Dropout on Performance in Long-range Graph Learning Tasks
Jasraj Singh, Keyue Jiang, Brooks Paige +1
Message Passing Neural Networks (MPNNs) are a class of Graph Neural Networks (GNNs) that propagate information across the graph via local neighborhoods. The scheme gives rise to tw…
Training-Free Message Passing for Learning on Hypergraphs
Bohan Tang, Zexi Liu, Keyue Jiang +2
Hypergraphs are crucial for modelling higher-order interactions in real-world data. Hypergraph neural networks (HNNs) effectively utilise these structures by message passing to gen…
A Markov Random Field model for Hypergraph-based Machine Learning
Bohan Tang, Keyue Jiang, Laura Toni +2
Understanding the data-generating process is essential for building machine learning models that generalise well while ensuring robustness and interpretability. This paper addresse…