Auto-calibration of forecast sequences equals measure-valued martingales, enabling a statistical test for calibration of updating predictions.
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Introduces Trajectory Proper Score (TPS) as a strictly proper family of trajectory-level scoring rules that elicits the complete prefix-conditioned success probability process.
A single end-to-end Transformer model unifies stellar labels from heterogeneous spectroscopic surveys into a self-consistent scale without post-hoc recalibration.
Stochastic Attention adds calibrated uncertainty to transformer foundation models through inference-time multinomial sampling of attention weights and univariate post-hoc tuning of a concentration parameter.
RECAP benchmark finds that six prompt optimization methods show no significant performance gains under proactive continual adaptation to evolving constraints across four LLMs.
Neural network classification with CRPS optimization produces calibrated photometric redshift PDFs for DESI Legacy and Pan-STARRS data, achieving σ_NMAD of 0.0153 on LSDR10 and outperforming regression methods.
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Calibrated Probability Forecast Sequences and Measure-Valued Martingales
Auto-calibration of forecast sequences equals measure-valued martingales, enabling a statistical test for calibration of updating predictions.