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20242026
most citedSAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks

5 citations · 6 across the 5 of their papers we have counts for

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cs.LG2026

Principled Koopman Representations with Kalman Inference for Efficient Time-Series Prediction

Ruiquan Li, Yuheng Bu

The Koopman operator has been widely used for time-series prediction in dynamical systems. However, prior work that learns latent ``Koopman spaces'' using neural networks often did…

cs.LG2026

TrustEnergy: A Unified Framework for Accurate and Reliable User-level Energy Usage Prediction

Dahai Yu, Rongchao Xu, Dingyi Zhuang +3

Energy usage prediction is important for various real-world applications, including grid management, infrastructure planning, and disaster response. Although a plethora of deep lea…