42 citations · 114 across the 10 of their papers we have counts for
10 papers
MetaOOD: Automatic Selection of OOD Detection Models
Yuehan Qin, Yichi Zhang, Yi Nian +2
How can we automatically select an out-of-distribution (OOD) detection model for various underlying tasks? This is crucial for maintaining the reliability of open-world application…
PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks
Zhiyuan Zhao, Xueying Ding, B. Aditya Prakash
Physics-Informed Neural Networks (PINNs) have emerged as a promising deep learning framework for approximating numerical solutions to partial differential equations (PDEs). However…
Fast Unsupervised Deep Outlier Model Selection with Hypernetworks
Xueying Ding, Yue Zhao, Leman Akoglu
Outlier detection (OD) finds many applications with a rich literature of numerous techniques. Deep neural network based OD (DOD) has seen a recent surge of attention thanks to the…
From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management
Xueying Ding, Nikita Seleznev, Senthil Kumar +2
Anomalies are often indicators of malfunction or inefficiency in various systems such as manufacturing, healthcare, finance, surveillance, to name a few. While the literature is ab…
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…
Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solution
Xueying Ding, Lingxiao Zhao, Leman Akoglu
Outlier detection (OD) literature exhibits numerous algorithms as it applies to diverse domains. However, given a new detection task, it is unclear how to choose an algorithm to us…
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…
Causal inference using deep neural networks
Ye Yuan, Xueying Ding, Ziv Bar-Joseph
Causal inference from observation data is a core problem in many scientific fields. Here we present a general supervised deep learning framework that infers causal interactions by…
SUOD: Toward Scalable Unsupervised Outlier Detection
Yue Zhao, Xueying Ding, Jianing Yang +1
Outlier detection is a key field of machine learning for identifying abnormal data objects. Due to the high expense of acquiring ground truth, unsupervised models are often chosen…
Combining Machine Learning Models using combo Library
Yue Zhao, Xuejian Wang, Cheng Cheng +1
Model combination, often regarded as a key sub-field of ensemble learning, has been widely used in both academic research and industry applications. To facilitate this process, we…