2 citations · 5 across the 11 of their papers we have counts for
11 papers · 1 filter
Structured Neural Marked Point Processes for Interpretable Event Interaction Modeling
Zhitong Xu, Qiwei Yuan, Yinghao Chen +2
Multi-class event streams arise in numerous real-world applications, where uncovering structured, interpretable inter-event relationships, together with accurate prediction, remain…
Arbitrarily Conditioned Hierarchical Flows for Spatiotemporal Events
Keyan Chen, Qiwei Yuan, Zhitong Xu +2
Events in spatiotemporal systems are ubiquitous, yet modeling their complex distributions remains challenging. Existing point process models often rely on strong structural assumpt…
Focus and Dilution: The Multi-stage Learning Process of Attention
Zheng-An Chen, Pengxiao Lin, Zhi-Qin John Xu +1
Transformer-based models have achieved remarkable success across a wide range of domains, yet our understanding of their training dynamics remains limited. In this work, we identif…
Kronecker-Structured Nonparametric Spatiotemporal Point Processes
Zhitong Xu, Qiwei Yuan, Yinghao Chen +3
Events in spatiotemporal domains arise in numerous real-world applications, where uncovering event relationships and enabling accurate prediction are central challenges. Classical…
Deep Gaussian Processes for Functional Maps
Matthew Lowery, Zhitong Xu, Da Long +5
Learning mappings between functional spaces, also known as function-on-function regression, is a fundamental problem in functional data analysis with broad applications, including…
Tensor Gaussian Processes: Efficient Solvers for Nonlinear PDEs
Qiwei Yuan, Zhitong Xu, Yinghao Chen +3
Machine learning solvers for partial differential equations (PDEs) have attracted growing interest. However, most existing approaches, such as neural network solvers, rely on stoch…
Pseudo-Physics-Informed Neural Operators: Enhancing Operator Learning from Limited Data
Keyan Chen, Yile Li, Da Long +4
Neural operators have shown great potential in surrogate modeling. However, training a well-performing neural operator typically requires a substantial amount of data, which can po…
Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation
Da Long, Zhitong Xu, Guang Yang +2
Modern physics simulation often involves multiple functions of interests, and traditional numerical approaches are known to be complex and computationally costly. While machine lea…
Toward Efficient Kernel-Based Solvers for Nonlinear PDEs
Zhitong Xu, Da Long, Yiming Xu +3
We introduce a novel kernel learning framework toward efficiently solving nonlinear partial differential equations (PDEs). In contrast to the state-of-the-art kernel solver that em…
Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems
Da Long, Zhitong Xu, Qiwei Yuan +2
Fourier Neural Operator (FNO) is a powerful and popular operator learning method. However, FNO is mainly used in forward prediction, yet a great many applications rely on solving i…
Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization
Zhitong Xu, Haitao Wang, Jeff M Phillips +1
A long-standing belief holds that Bayesian Optimization (BO) with standard Gaussian processes (GP) -- referred to as standard BO -- underperforms in high-dimensional optimization p…