uncertainty-aware refinement

Approximate Bayesian inference often underestimates true uncertainty due to posterior credible intervals that are excessively narrow. This work proposes two simulation-based calibration (SBC)-driven methods for recalibrating approximate posteriors, systematically leveraging the SBC framework to adjust the width of posterior uncertainty intervals and achieve marginal calibration. The approach is applicable to complex model structures, including hierarchical models, and demonstrates consistent efficacy across diverse experimental settings by meaningfully widening posterior intervals. As a result, the proposed recalibration substantially enhances the calibration accuracy and reliability of approximate Bayesian inference.

This work identifies insufficient uncertainty calibration as a critical impediment to perturbation-based explanation methods (e.g., LIME, SHAP): systematic distortion in model probability estimates under explanatory perturbations degrades both the reliability and stability of local and global interpretations. To address this, we establish—for the first time—theoretical links between calibration quality and explanation quality. We then propose ReCalX, a novel recalibration framework specifically designed for explanation scenarios, which optimizes output confidence under perturbations without altering original predictions. Extensive experiments demonstrate that ReCalX significantly reduces perturbation-specific calibration error and consistently improves feature importance identification accuracy and explanation robustness across diverse models and datasets. By providing a verifiable, explanation-aware calibration paradigm, ReCalX advances the foundation for trustworthy model interpretation.

本文针对模型信息不足时的可靠性集合估计问题，提出一种结合校准过程和自适应设计的统一框架，以降低决策风险并控制误包含风险。

Perturbation-based explanation methods suffer from limited reliability due to miscalibrated probability estimates under input perturbations—a fundamental issue stemming from the lack of uncertainty calibration tailored to explainability scenarios. This work establishes, for the first time, a strong empirical and theoretical link between uncertainty calibration and perturbation explanation quality. We propose ReCalX, a post-hoc calibration framework specifically designed for explainability-oriented perturbations. Without altering the original model’s predictions, ReCalX recalibrates confidence distributions over perturbed inputs via an explanation-aware uncertainty alignment mechanism. Integrating principles from probabilistic calibration theory with perturbation sensitivity analysis, ReCalX significantly improves alignment between explanations and human cognition as well as ground-truth object locations. Extensive experiments across multiple benchmarks demonstrate consistent improvements in explanation credibility, stability, and standard interpretability evaluation metrics.

Model uncertainty suffers from conceptual ambiguity and inconsistent definitions, undermining the reliability of statistical inference. This paper addresses this issue by conducting a systematic literature review and proposing a novel threefold framework that distinguishes (i) *true model uncertainty*—arising from unknown data-generating mechanisms; (ii) *model selection uncertainty*—stemming from a finite set of candidate models; and (iii) *model selection instability*—characterized by drastic changes in selected models under minor data perturbations. Through conceptual analysis, literature synthesis, and illustrative examples, we demonstrate how neglecting these distinct sources adversely affects standard errors, confidence intervals, and hypothesis tests. Drawing on statistical inference theory, we further discuss targeted mitigation strategies. The framework provides a unified, operationally grounded theoretical foundation for model uncertainty, enhancing both robustness and interpretability of inference in complex modeling settings.

This study addresses the computational bottleneck in evaluating the calibration of nested uncertainty sets within expensive simulation models. To overcome this limitation, the work proposes an efficient calibration assessment method grounded in a Bayesian framework and the Dirichlet-Multinomial model. By exploiting the nested structure, the approach directly processes interval outputs without requiring access to the full predictive distribution. Furthermore, it incorporates Bayes factor testing for statistical inference, substantially reducing the number of independent simulations needed. The proposed method successfully detects model miscalibration in data assimilation tasks under limited simulation budgets. Overall, this work significantly lowers computational costs while demonstrating both the effectiveness and practical utility of the proposed approach for calibrating complex simulation systems.

This work addresses Bayesian optimal experimental design under computationally expensive models with limited design evaluations. It proposes an adaptive sequential elimination algorithm that significantly reduces the variance and computational cost of nested Monte Carlo estimators by reusing parameter samples, employing common random numbers, and applying Rao–Blackwellization. A bootstrap-based probabilistic comparison mechanism is integrated to iteratively eliminate inferior designs. The method achieves high reliability while drastically reducing the number of model evaluations, making it well-suited for large-scale engineering applications where computational efficiency and decision accuracy must be carefully balanced.

论文提出Evidence-Calibration-Stability框架，通过区分证据、校准和稳定性来解决模型不确定性下的假设检验问题。

This work addresses the challenge of selecting uncertainty representations that align with decision objectives to achieve optimal and trustworthy decisions under state-variable uncertainty. Drawing on decision theory, it systematically analyzes the optimal forms of uncertainty representation for both risk-neutral and risk-averse agents in known and unknown environments, revealing the minimal uncertainty information required under distinct risk preferences. The study innovatively unifies three approaches to epistemic uncertainty—calibrated prediction, confidence-set robust optimization, and Bayesian inference—establishing a theoretical link between uncertainty representation and decision goals. This integration yields a reliable decision-making framework that provides agents with verifiable utility guarantees.

This study addresses the challenge of adaptively determining when to reset a model’s structure under lightweight parameter update strategies to balance predictive accuracy, computational cost, and stability. The authors propose a “model specification debt” mechanism that accumulates evidence—such as prediction score discrepancies, stacked weights, or calibration diagnostics—to formulate a cost-sensitive trigger rule for model resetting. This framework generalizes fixed-interval updating as a special case and enables flexible deployment in open environments. Evaluated on the M4 dataset, the approach achieves predictive accuracy comparable to full retraining while consuming only 28% of the computation time, significantly reducing instability. It consistently matches or outperforms fixed-update strategies across diverse scenarios and offers dynamic, evidence-driven adaptation capabilities.