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stat.ML2026
Keeping Score: Adaptive, Tuning-Free Loss Weighting for Score-Augmented Neural Ratio Estimation
Alexander Shen, Mikael Kuusela
Neural likelihood surrogates (e.g., Neural Ratio Estimation) for stochastic process models are commonly trained via probabilistic classification on simulated data, which forces a t…
stat.AP2026
Downscaling land surface temperature data using edge detection and block-diagonal Gaussian process regression
Sanjit Dandapanthula, Margaret Johnson, Madeleine Pascolini-Campbell +2
Accurate and high-resolution estimation of land surface temperature (LST) is crucial in estimating evapotranspiration, a measure of plant water use and a central quantity in agricu…