Author

Ying Li

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#machine learning Preprint Oct 2026

Random Feature Gaussian Process Attention: Linear-Time Probabilistic Attention with Calibrated Uncertainty

Transformers provide a state-of-the-art modeling framework, yet poor calibration limits their reliability in safety-critical applications. A promising direction addresses this issue by interpreting attention as a Gaussian process (GP) posterior, which enables principled uncertainty calibration but incurs cubic complexi...

Amir Mohammad Mahfoozi, Zi-Ye Yang, Ying Li et al. · 0 citations

#machine learning Preprint Sep 2026

Simulation-Free Learning of GP-SDEs from Irregular Observations

This work analytically marginalize the sparse GP posterior to derive a tractable drift-matching objective that accounts for both the posterior mean and uncertainty of the unknown drift, and introduces an irregular-time-aware variational state posterior that incorporates the actual observation times during both encoding...

Zhi-Di Lin, Yu-Hao Liu, Ying Li et al. · 0 citations