2024/09/27 by Charles G. Marx, Volodymyr Kuleshov, Marx, Charles +3 · 1 citation
Decision Sciences · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Simulation Techniques and Applications
paper · pdf · doi:10.48550/arxiv.2409.19157
openalex publication_date 2024/09/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Real-world data streams can change unpredictably due to distribution shifts, feedback loops and adversarial actors, which challenges the validity of forecasts. We present a forecasting framework ensuring valid uncertainty estimates regardless of how data evolves. Leveraging the concept of Blackwell approachability from game theory, we introduce a forecasting framework that guarantees calibrated uncertainties for outcomes in any compact space (e.g., classification or bounded regression). We extend this framework to recalibrate existing forecasters, guaranteeing calibration without sacrificing predictive performance. We implement both general-purpose gradient-based algorithms and algorithms optimized for popular special cases of our framework. Empirically, our algorithms improve calibration and downstream decision-making for energy systems.