Conformal Prediction for Regression with Heteroscedasticity, Multimodality, and Skewness
Summary:
A paper accepted at the workshop on Regulatable ML at NeurIPS 2023 discusses the challenges of using Conformal Prediction (CP) in regression tasks, especially when dealing with heteroscedasticity, multimodality, or skewness in the output distribution. While estimating a distribution over the output can address some of these issues, such approaches can be sensitive to estimation error and yield unstable intervals. The paper proposes a new approach that circumvents these problems by using transformation-based conformal prediction. Experimental results show that the proposed method outperforms existing approaches in terms of prediction accuracy and interval stability.
Key Points:
- Conformal Prediction (CP) is a method used to estimate risk or uncertainty in Machine Learning applications complying with Risk Management regulations.
- CP can be challenging in regression tasks, especially when the output distribution is heteroscedastic, multimodal, or skewed.
- Estimating a distribution over the output can mitigate some issues but may be sensitive to estimation error and yield unstable intervals.
- To address these problems, the paper introduces a new approach that uses transformation-based conformal prediction.
- Experimental results demonstrate that the proposed method outperforms existing approaches in terms of prediction accuracy and interval stability.
Author’s Take: The paper presents a novel approach to Conformal Prediction for regression tasks with challenging output distributions. By using transformation-based conformal prediction, the proposed method addresses the issues of heteroscedasticity, multimodality, and skewness, while outperforming existing approaches in terms of prediction accuracy and interval stability. This research has the potential to contribute to improved risk estimation and compliance with regulations in fields such as healthcare and finance.