12 citations · 34 across the 12 of their papers we have counts for
10 papers · 1 filter
Diffusion Transformers are Provably Optimal In-context Generators
Guoji Fu, Tomoya Wakayama, Ryotaro Kawata +3
Generative foundation models are attracting interest for their ability to produce desired outputs from demonstrations given at inference time, without updating parameters. However,…
Intrinsic Wasserstein Rates for Score-Based Generative Models on Smooth Manifolds
Guoji Fu, Taiji Suzuki, Wee Sun Lee +1
Score-based generative models are trained in high-dimensional ambient spaces, yet many data distributions are supported on low-dimensional nonlinear structures. We prove that, for…
Continual Reinforcement Learning by Planning with Online World Models
Zichen Liu, Guoji Fu, Chao Du +2
Continual reinforcement learning (CRL) refers to a naturalistic setting where an agent needs to endlessly evolve, by trial and error, to solve multiple tasks that are presented seq…
Exploration by Random Reward Perturbation
Haozhe Ma, Guoji Fu, Zhengding Luo +2
We introduce Random Reward Perturbation (RRP), a novel exploration strategy for reinforcement learning (RL). Our theoretical analyses demonstrate that adding zero-mean noise to env…
Approximation and Generalization Abilities of Score-based Neural Network Generative Models for Sub-Gaussian Distributions
Guoji Fu, Wee Sun Lee
This paper studies the approximation and generalization abilities of score-based neural network generative models (SGMs) in estimating an unknown distribution from i.i.d.…
Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-Smoothing
Guoji Fu, Mohammed Haroon Dupty, Yanfei Dong +1
Implicit Graph Neural Networks (GNNs) have achieved significant success in addressing graph learning problems recently. However, poorly designed implicit GNN layers may have limite…
Fairness-aware Message Passing for Graph Neural Networks
Huaisheng Zhu, Guoji Fu, Zhimeng Guo +3
Graph Neural Networks (GNNs) have shown great power in various domains. However, their predictions may inherit societal biases on sensitive attributes, limiting their adoption in r…
-Laplacian Based Graph Neural Networks
Guoji Fu, Peilin Zhao, Yatao Bian
Graph neural networks (GNNs) have demonstrated superior performance for semi-supervised node classification on graphs, as a result of their ability to exploit node features and top…
Understanding Graph Neural Networks from Graph Signal Denoising Perspectives
Guoji Fu, Yifan Hou, Jian Zhang +3
Graph neural networks (GNNs) have attracted much attention because of their excellent performance on tasks such as node classification. However, there is inadequate understanding o…
Representation Learning for Heterogeneous Information Networks via Embedding Events
Guoji Fu, Bo Yuan, Qiqi Duan +1
Network representation learning (NRL) has been widely used to help analyze large-scale networks through mapping original networks into a low-dimensional vector space. However, exis…