42 citations · 84 across the 9 of their papers we have counts for
10 papers
QiYao-I: A Manifold Based Foundation Model for Irregular Multivariate Time Series Forecasting
Linfeng Wang, Ruitong Zhang, Kai Zhao +7
Irregular multivariate time series forecasting is a challenging yet important problem in real-world applications, where observations are often irregularly sampled and asynchronousl…
Forward-Evolution Error Analysis and Adaptive Design for Matrix-Valued Diffusion Models
Tongyao Pang, Zuowei Shen, Ruitong Zhang
Diffusion models learn to reverse a predefined corruption process, but sampling still requires a costly time discretization and depends on the chosen noise schedule. We study these…
DREAM Technical Report
Bin Zhang, Bowen Zheng, Chao Yi +74
Industrial recommender systems commonly use cascaded retrieval, ranking, and re-ranking pipelines. Although efficient, these pipelines fragment information and objectives across mo…
Adaptive Kernel Density Estimation with Pre-training
Ruitong Zhang, Ke Deng
Density estimation in high-dimensional settings is an important and challenging statistical problem.Traditional methods based on kernel smoothing are inefficient in high dimensions…
STAR: Boosting Time Series Foundation Models for Anomaly Detection through State-aware Adapter
Hanyin Cheng, Ruitong Zhang, Yuning Lu +5
While Time Series Foundation Models (TSFMs) have demonstrated remarkable success in Multivariate Time Series Anomaly Detection (MTSAD), however, in real-world industrial scenarios,…
Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning
Hao Peng, Xiang Huang, Shuo Sun +2
DBSCAN, a well-known density-based clustering algorithm, has gained widespread popularity and usage due to its effectiveness in identifying clusters of arbitrary shapes and handlin…
Automating DBSCAN via Deep Reinforcement Learning
Ruitong Zhang, Hao Peng, Yingtong Dou +4
DBSCAN is widely used in many scientific and engineering fields because of its simplicity and practicality. However, due to its high sensitivity parameters, the accuracy of the clu…
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
Kay Liu, Yingtong Dou, Yue Zhao +12
Detecting which nodes in graphs are outliers is a relatively new machine learning task with numerous applications. Despite the proliferation of algorithms developed in recent years…
PyGOD: A Python Library for Graph Outlier Detection
Kay Liu, Yingtong Dou, Xueying Ding +5
PyGOD is an open-source Python library for detecting outliers in graph data. As the first comprehensive library of its kind, PyGOD supports a wide array of leading graph-based meth…
Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks
Hao Peng, Ruitong Zhang, Yingtong Dou +3
Graph Neural Networks (GNNs) have been widely used for the representation learning of various structured graph data. While promising, most existing GNNs oversimplified the complexi…