29 citations · 110 across the 38 of their papers we have counts for
7 papers · 1 filter
Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective
Zexin Wang, Changhua Pei, Minghua Ma +9
Time series Anomaly Detection (AD) plays a crucial role for web systems. Various web systems rely on time series data to monitor and identify anomalies in real time, as well as to…
Contrastive Learning with Negative Sampling Correction
Lu Wang, Chao Du, Pu Zhao +8
As one of the most effective self-supervised representation learning methods, contrastive learning (CL) relies on multiple negative pairs to contrast against each positive pair. In…
COIN: Chance-Constrained Imitation Learning for Uncertainty-aware Adaptive Resource Oversubscription Policy
Lu Wang, Mayukh Das, Fangkai Yang +9
We address the challenge of learning safe and robust decision policies in presence of uncertainty in context of the real scientific problem of adaptive resource oversubscription to…
Robust Positive-Unlabeled Learning via Noise Negative Sample Self-correction
Zhangchi Zhu, Lu Wang, Pu Zhao +7
Learning from positive and unlabeled data is known as positive-unlabeled (PU) learning in literature and has attracted much attention in recent years. One common approach in PU lea…
ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection
Yuhang Chen, Chaoyun Zhang, Minghua Ma +7
Anomaly detection in multivariate time series data is of paramount importance for ensuring the efficient operation of large-scale systems across diverse domains. However, accuratel…
Conservative State Value Estimation for Offline Reinforcement Learning
Liting Chen, Jie Yan, Zhengdao Shao +5
Offline reinforcement learning faces a significant challenge of value over-estimation due to the distributional drift between the dataset and the current learned policy, leading to…
Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning
Junjie Sheng, Lu Wang, Fangkai Yang +9
Oversubscription is a common practice for improving cloud resource utilization. It allows the cloud service provider to sell more resources than the physical limit, assuming not al…