Zijun Dou — accepted AI-conference papers

2026

AutoDA-Timeseries: Automated Data Augmentation for Time Series

Zijun Dou, Zhenhe Yao, Zhe Xie, Xidao Wen, Tong Xiao, Dan Pei

ICLR 2026poster

Data augmentation is a fundamental technique in deep learning, widely applied in both representation learning and automated data augmentation (AutoDA). In representation learning, augmentations are used to construct contrastive views for learning task-agnostic embeddings, while in AutoDA the augment…

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2026

Taming the Recent-Data Bias: Towards Robust Time Series Forecasting with Global Context

Longlong Xu, Zeyan Li, Xiao He, Zhaoyang Yu, Changhua Pei, Zhe Xie +3

ICML 2026poster

Time series forecasting plays a vital role in numerous domains. However, real-world time series are frequently contaminated by noise, missing values, and anomalies, posing significant challenges to reliable forecasting. In this work, we first systematically investigate a fundamental limitation preva…

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