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cs.LG2026
Parallel Complex Diffusion for Scalable Time Series Generation
Rongyao Cai, Yuxi Wan, Kexin Zhang +4
Diffusion models learn data distributions indirectly through denoising, making the difficulty of generative modeling closely tied to the dependency structure of data. For time seri…
cs.LG2026
OATS: Online Data Augmentation for Time Series Foundation Models
Junwei Deng, Chang Xu, Jiaqi W. Ma +5
Time Series Foundation Models (TSFMs) are a powerful paradigm for time series analysis and are often enhanced by synthetic data augmentation to improve the training data quality. E…
cs.LG2024
Preparing for Black Swans: The Antifragility Imperative for Machine Learning
Ming Jin
Operating safely and reliably despite continual distribution shifts is vital for high-stakes machine learning applications. This paper builds upon the transformative concept of ``a…