activity
20162026
most citedKernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning

6 citations · 43 across the 39 of their papers we have counts for

collaborators

44 papers

cs.LG2026

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…

cs.LG2026

Dual-Agent Co-Training for Health Coaching via Implicit Adversarial Preference Optimization

Da Long, Lingyi Fu, Diya Michelle Rao +3

Motivational-interviewing-based health coaching is an effective approach for improving mental health and promoting healthy behavior change. However, the scarcity of trained human c…

cs.LG2026

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…

cs.LG2026

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…

stat.ML2025

A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications

Shridhar Vashishtha, Krishna Prasath Logakannan, Jacob Hochhalter +2

Digital twins are developed to model the behavior of a specific physical asset (or twin), and they can consist of high-fidelity physics-based models or surrogates. A highly accurat…

cs.LG2025

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…