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Introduces Q-srdrn, a multi-quantile super-resolution network using pinball loss to improve extreme precipitation downscaling, achieving dramatic detection rate gains for heavy rainfall events while maintaining overall accuracy.
This paper introduces RQIQN, a robust quantile-based method for distributional reinforcement learning that uses Wasserstein geometry regularization to prevent distribution degeneration and improve performance in risk-sensitive tasks.
Amazon and Stanford researchers propose Quantile Token Regression, inserting dedicated quantile tokens into LLM inputs to predict full probability distributions, achieving ~4 point MAPE reduction and 2× narrower intervals on Airbnb and Stack Overflow benchmarks.