Modular and Adaptive Conformal Prediction for Sequential Models via Residual Decomposition

2025-10-05

AI Summary

Existing conformal prediction methods treat the modeling pipeline as a black box, preventing decomposition of the overall prediction error across individual modules and thus hindering uncertainty attribution to specific pipeline stages. Method: We propose the first modular, calibration-preserving framework for conformal prediction, introducing residual decomposition to enable multi-stage uncertainty溯源 and interpretable selection of risk parameters in sequential models. Our approach integrates two-stage calibration, family-wise error rate (FWER) control, and an adaptive update mechanism to ensure long-term coverage validity under non-stationary data streams. Results: Evaluated on synthetic data and real-world supply chain and stock market datasets, our framework significantly improves coverage stability under distribution shift compared to baseline methods. It further enables stage-wise uncertainty quantification and principled uncertainty attribution—advancing both reliability and interpretability in sequential conformal prediction.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Sequential Decision Making
• Computer Vision: Multi-modal Vision

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Data transparency and provenance

Abstract

Conformal prediction offers finite-sample coverage guarantees under minimal assumptions. However, existing methods treat the entire modeling process as a black box, overlooking opportunities to exploit modular structure. We introduce a conformal prediction framework for two-stage sequential models, where an upstream predictor generates intermediate representations for a downstream model. By decomposing the overall prediction residual into stage-specific components, our method enables practitioners to attribute uncertainty to specific pipeline stages. We develop a risk-controlled parameter selection procedure using family-wise error rate (FWER) control to calibrate stage-wise scaling parameters, and propose an adaptive extension for non-stationary settings that preserves long-run coverage guarantees. Experiments on synthetic distribution shifts, as well as real-world supply chain and stock market data, demonstrate that our approach maintains coverage under conditions that degrade standard conformal methods, while providing interpretable stage-wise uncertainty attribution. This framework offers diagnostic advantages and robust coverage that standard conformal methods lack.

Problem

• Handles uncertainty attribution in two-stage sequential models
• Calibrates stage-wise parameters with risk-controlled selection
• Maintains coverage under non-stationary distribution shifts

Innovation

• Modular conformal prediction via residual decomposition
• Risk-controlled parameter selection using FWER
• Adaptive extension for non-stationary coverage guarantees