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stat.ML2026
Towards Calibrated Probabilistic Forecasts for Events of Interest via Outcome-Conditional Recalibration
Jakob Benjamin Wessel, Sam Allen
Calibration is an essential requirement for probabilistic predictions to be useful for decision making. While state-of-the-art prediction methods often yield miscalibrated predicti…
stat.ML2024
Efficient pooling of predictions via kernel embeddings
Sam Allen, David Ginsbourger, Johanna Ziegel
Probabilistic predictions are probability distributions over the set of possible outcomes. Such predictions quantify the uncertainty in the outcome, making them essential for effec…