no code implementations • 18 Jan 2024 • Jingchao Ni, Gauthier Guinet, Peihong Jiang, Laurent Callot, Andrey Kan
We begin by identifying the challenges unique to this anomaly detection problem, which is at entity-level (e. g., deployments), relative to the more typical problem of anomaly detection in multivariate time series (MTS).
1 code implementation • 3 Oct 2022 • Mononito Goswami, Cristian Challu, Laurent Callot, Lenon Minorics, Andrey Kan
The practical problem of selecting the most accurate model for a given dataset without labels has received little attention in the literature.
no code implementations • 18 Jan 2022 • Christian Bock, François-Xavier Aubet, Jan Gasthaus, Andrey Kan, Ming Chen, Laurent Callot
We propose r-ssGPFA, an unsupervised online anomaly detection model for uni- and multivariate time series building on the efficient state space formulation of Gaussian processes.
no code implementations • 11 Nov 2020 • Namyong Park, Andrey Kan, Christos Faloutsos, Xin Luna Dong
Online recommendation is an essential functionality across a variety of services, including e-commerce and video streaming, where items to buy, watch, or read are suggested to users.
no code implementations • 24 Jun 2020 • Xin Luna Dong, Xiang He, Andrey Kan, Xi-An Li, Yan Liang, Jun Ma, Yifan Ethan Xu, Chenwei Zhang, Tong Zhao, Gabriel Blanco Saldana, Saurabh Deshpande, Alexandre Michetti Manduca, Jay Ren, Surender Pal Singh, Fan Xiao, Haw-Shiuan Chang, Giannis Karamanolakis, Yuning Mao, Yaqing Wang, Christos Faloutsos, Andrew McCallum, Jiawei Han
Can one build a knowledge graph (KG) for all products in the world?
no code implementations • 22 Jun 2020 • Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos
MultiImport is a latent variable model that captures the relation between node importance and input signals, and effectively learns from multiple signals with potential conflicts.
no code implementations • 18 Jun 2020 • Yuning Mao, Tong Zhao, Andrey Kan, Chenwei Zhang, Xin Luna Dong, Christos Faloutsos, Jiawei Han
We propose to distantly train a sequence labeling model for term extraction and employ graph neural networks (GNNs) to capture the taxonomy structure as well as the query-item-taxonomy interactions for term attachment.
no code implementations • 21 May 2019 • Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos
How can we estimate the importance of nodes in a knowledge graph (KG)?