Decoupled Conformal Optimisation: Efficient Prediction Sets via Independent Tuning and Calibration
Researchers are advancing conformal prediction (CP) techniques to improve uncertainty quantification and fairness in machine learning. New methods like FedCF aim to extend CP to federated learning settings, enabling fairness audits across different subgroups. Other advancements include DistMatch for robust sequential CP in time series, SpeedCP for efficient kernel-based conditional CP, and DCO for decoupled optimization of prediction sets. Additionally, new diagnostics like ERT are being developed to better evaluate conditional coverage, and research is exploring substantive fairness beyond procedural guarantees. AI
IMPACT These advancements in conformal prediction offer improved methods for uncertainty quantification, fairness, and robustness, crucial for reliable AI deployment in sensitive applications.
- Concrete
- ImageNet-A
- CIFAR-100
- Diabetes
- Hugging Face
- arXiv
- Decomposition-Based Modular Conformal Prediction
- William Zhang
- Conformal Prediction
- Guillaume Principato
- Sacha Braun Mr
- SpeedCP
- Gibbs et al. (2023)
- Decoupled Conformal Optimisation (DCO)
- Yao Zhang
- Yating Liu
- Machine Learning
- FedCF
- DistMatch
- Federated Learning