Summary of Calibrated Probabilistic Forecasts For Arbitrary Sequences, by Charles Marx et al.
Calibrated Probabilistic Forecasts for Arbitrary Sequences
by Charles Marx, Volodymyr Kuleshov, Stefano Ermon
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a forecasting framework that ensures valid uncertainty estimates in real-world data streams. The framework leverages the concept of Blackwell approachability from game theory to guarantee calibrated uncertainties for outcomes in any compact space, such as classification or bounded regression. The authors extend this framework to recalibrate existing forecasters, ensuring calibration without sacrificing predictive performance. They implement both general-purpose gradient-based algorithms and optimized algorithms for popular special cases. Empirically, the proposed algorithms improve calibration and downstream decision-making for energy systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to predict what will happen in the future based on data that might change unexpectedly. This paper helps solve this problem by creating a forecasting system that always provides accurate uncertainty estimates. The system works by using a concept from game theory to ensure that its predictions are correct, no matter how the data changes. The authors also show how to improve existing forecasting systems using their new approach. They tested their algorithms on energy systems and found that they improved the accuracy of predictions and helped make better decisions. |
Keywords
» Artificial intelligence » Classification » Regression
