78 citations · 98 across the 6 of their papers we have counts for
6 papers
Context-aware Domain Adaptation for Time Series Anomaly Detection
Kwei-Herng Lai, Lan Wang, Huiyuan Chen +4
Time series anomaly detection is a challenging task with a wide range of real-world applications. Due to label sparsity, training a deep anomaly detector often relies on unsupervis…
Denoising Self-attentive Sequential Recommendation
Huiyuan Chen, Yusan Lin, Menghai Pan +6
Transformer-based sequential recommenders are very powerful for capturing both short-term and long-term sequential item dependencies. This is mainly attributed to their unique self…
Do learned representations respect causal relationships?
Lan Wang, Vishnu Naresh Boddeti
Data often has many semantic attributes that are causally associated with each other. But do attribute-specific learned representations of data also respect the same causal relatio…
Forecast-based Multi-aspect Framework for Multivariate Time-series Anomaly Detection
Lan Wang, Yusan Lin, Yuhang Wu +3
Today's cyber-world is vastly multivariate. Metrics collected at extreme varieties demand multivariate algorithms to properly detect anomalies. However, forecast-based algorithms,…
Event2Graph: Event-driven Bipartite Graph for Multivariate Time-series Anomaly Detection
Yuhang Wu, Mengting Gu, Lan Wang +3
Modeling inter-dependencies between time-series is the key to achieve high performance in anomaly detection for multivariate time-series data. The de-facto solution to model the de…
Generalization bounds via distillation
Daniel Hsu, Ziwei Ji, Matus Telgarsky +1
This paper theoretically investigates the following empirical phenomenon: given a high-complexity network with poor generalization bounds, one can distill it into a network with ne…