Quantile Regression for Aleatoric Uncertainty

• Quantile regression for aleatoric uncertainty explicitly models conditional quantiles to capture non-Gaussian and heteroscedastic data noise.
• It employs techniques like pinball loss and ensemble methods to construct calibrated, sharp prediction intervals in various models.
• Applications range from quality control and hydrology to autonomous systems, demonstrating its practical utility in diverse domains.

Quantile regression for aleatoric uncertainty refers to statistical and machine learning methodologies that explicitly model the conditional quantiles of a target variable as a means of capturing and quantifying irreducible data noise—the “aleatoric” component of predictive uncertainty. Unlike approaches that characterize only central tendencies or assume homoscedasticity, quantile regression provides a distributional summary that can flexibly represent non-Gaussian, heteroscedastic, and asymmetric noise. Modern research advances have extended its reach from classical linear models to deep learning, ensemble methods, Bayesian frameworks, and high-dimensional settings, with rigorous tools for calibration, robustness, and separation of uncertainty sources.

1. Principles of Quantile Regression for Aleatoric Uncertainty

Quantile regression estimates the τ-th conditional quantile of the response $Y$ given features $X = x$, denoted $Q_\tau(Y|X = x)$, by solving:

$$\min_\theta \sum_{i} \rho_\tau(y_i - f_\theta(x_i)),$$

where the pinball (check) loss is

$\rho_\tau(u) = \max\{\tau u, ( \tau - 1 ) u\}.$

This paradigm provides prediction intervals $[Q_{\alpha/2}(Y|x), Q_{1-\alpha/2}(Y|x)]$ directly reflecting the spread in $Y$ due to inherent randomness—aleatoric uncertainty.

Foundational approaches such as the Regression Approach for Quantile Estimation (RAQE) focus on robustly modeling the tails of the data distribution using locally weighted regression on augmented empirical distributions, with explicit weighting to account for the inherent variability in quantile estimates at extreme regions (Salazar-Alvarez et al., 2017).

Quantile regression differs from variance-based measures (e.g., mean-squared error) by not presuming symmetry or Gaussianity, making it well-suited for nonparametric quantification of noise.

2. Calibration, Sharpness, and Robust Interval Construction

A central concern in quantile regression for uncertainty quantification is the calibration of prediction intervals—ensuring that empirical coverage closely matches the nominal level—and sharpness, i.e., the tightness of intervals.

The standard pinball loss implicitly trades off calibration and sharpness. Alternative frameworks explicitly decouple these objectives, as in the combined calibration-sharpness loss:

$$\mathcal{L}(D, \hat{Q}_p, p) = (1-\lambda)\mathcal{C}(D, \hat{Q}_p, p) + \lambda\mathcal{P}(D, \hat{Q}_p, p),$$

where $\mathcal{C}$ enforces calibration (the quantile is correct if $P(Y \leq \hat{Q}_p) \approx p$), $\mathcal{P}$ penalizes interval width, and $\lambda$ explicitly selects the tradeoff (Chung et al., 2020).

Interval score (Winkler score)–based losses

$$S_\alpha(\hat{L}, \hat{U}; y) = (\hat{U} - \hat{L}) + \frac{2}{\alpha}(L - y)\mathbb{I}\{y < \hat{L}\} + \frac{2}{\alpha}(y - \hat{U})\mathbb{I}\{ y > \hat{U}\}$$

reward models for producing intervals that are narrow but well calibrated (Chung et al., 2020).

Conformalized quantile regression (CQR) and its variants further adapt and calibrate prediction intervals in a distribution-free manner, correcting deficiencies in finite-sample coverage and providing robustness to model misspecification (Rossellini et al., 2023). Extensions explicitly separate aleatoric and epistemic components for more nuanced uncertainty estimation (see Section 5).

3. Algorithms and Modeling Frameworks

Classical and Augmented Empirical Approaches

RAQE augments the empirical distribution function with intermediate points and fits regression models in the tails, employing weights inversely proportional to the variance of cumulative probabilities, $w_i = n/(b_i(1-b_i))$, to handle higher variance in estimated probabilities in tail regions (Salazar-Alvarez et al., 2017). This approach has demonstrated superior mean square error in tail estimation for applications such as quality control and hydrology.

Deep and Ensemble Techniques

Modern neural methods integrate quantile regression into deep learning architectures by minimizing pinball loss at desired quantile levels, sometimes in conjunction with physical constraints or auxiliary losses (e.g., enforcing PDEs in temperature reconstruction) (Zheng et al., 2022, Zheng et al., 2022). Ensemble quantile regression frameworks (E-QR) and multi-quantile boosting ensembles (EMQ) further enhance the reliability and interpretability of conditional distribution predictions, incorporating mechanisms for monotonicity and adaptive flexibility (Yan et al., 2022, Ansari et al., 18 Dec 2024).

Ensemble-based approaches naturally decompose aleatoric uncertainty (spread within a model’s output) and epistemic uncertainty (spread across model configurations or data subsets). For instance, an E-QR ensemble can iteratively sample and retrain in regions of high uncertainty to separate these two contributions in a scalable fashion (Ansari et al., 18 Dec 2024).

Tree-Based and Gradient-Boosted Methods

Quantile regression forests (QRF) and variants relying on random forest proximities provide fully nonparametric estimates of $P(y|x)$ by constructing empirical conditional distributions using weights defined by tree traversals or refined proximity measures such as RF-GAP (Li et al., 5 Aug 2024). These approaches efficiently capture nonlinearity, heteroscedasticity, and complex feature interactions, enabling fast, robust estimation of aleatoric uncertainty across applications—from environmental modeling to finance.

Gradient boosting machines (e.g., QXGBoost) extend quantile regression to boosting by incorporating a Huber-smoothed, differentiable quantile loss, allowing optimization via gradient and Hessian computations and supporting efficient, parallel computation. Such frameworks yield probabilistic prediction intervals directly compatible with established machine learning toolkits (Yin et al., 2023).

Bayesian and High-Dimensional Approaches

Bayesian quantile regression and pseudo-Bayesian strategies leverage priors (e.g., scaled Student-t) and sampling schemes (e.g., Langevin Monte Carlo) to estimate conditional quantiles with uncertainty estimates naturally encoding model (epistemic) uncertainty. PAC-Bayes bounds provide non-asymptotic guarantees for prediction accuracy and adaptation to sparsity (Mai, 3 Sep 2024).

Closed-form expressions for quantile focused squared error loss enable efficient uncertainty quantification and variable subset selection in high-dimensional settings, with applications demonstrated on educational and genomics data (Feldman et al., 2023).

Robustness to Outliers and Feature Contamination

Beta-quantile regression (β-QR) introduces a robust divergence to down-weight the contribution of samples with large residuals, thereby mitigating the effects of outlier features and yielding more reliable aleatoric uncertainty estimates in deep learning settings such as image translation and anomaly detection (Akrami et al., 2023).

4. Applications Across Scientific and Industrial Domains

Quantile regression for aleatoric uncertainty is applied across diverse fields:

• Quality Control and Industrial Process Monitoring: Accurate quantile estimation of measurement distributions, especially in the tails, is used to set control limits and assess capability, as in wafer particle counts (Salazar-Alvarez et al., 2017).
• Hydrology and Environmental Science: Estimating extreme precipitation quantiles, return periods, and risk intervals with robust aggregation or ensemble approaches (Salazar-Alvarez et al., 2017, Fakoor et al., 2021).
• Medical Image Analysis: Uncertainty-aware lesion detection and segmentation, using deep quantile regression and extensions (e.g., QR-VAE, binary quantile regression) to assess confidence intervals in unsupervised and supervised medical imaging (Akrami et al., 2021).
• Physics-Informed Modeling: Surrogate models for temperature field reconstruction in engineering applications leverage quantile regression with physical priors, enabling estimation and propagation of measurement-induced uncertainty through downstream models (e.g., Bayesian networks for reliability analysis) (Zheng et al., 2022, Zheng et al., 2022).
• Finance and Market Risk: Quantile regression forests using random forest proximities enable efficient estimation of prediction intervals for quantities like trading volume, allowing risk managers to assess liquidity and volatility in real time (Li et al., 5 Aug 2024).
• Autonomous Systems and Reinforcement Learning: Distributional RL with implicit quantile networks (IQN) captures aleatoric return distributions, guiding risk-sensitive decision-making in safety-critical tasks such as autonomous driving (Hoel et al., 2021).
• High-Dimensional Prediction and Genomics: Sparse PAC-Bayesian quantile techniques yield minimax-optimal prediction and uncertainty quantification even under heavy-tailed noise, suitable for genomics and spectroscopy (Mai, 3 Sep 2024).

A summary table illustrates model classes and corresponding uncertainty estimation features:

Framework	Aleatoric Estimation Mechanism	Epistemic Handling
RAQE (Salazar-Alvarez et al., 2017)	Tail-weighted regression, e.d.f. var	—
Deep MC-QR (Zheng et al., 2022)	Pinball loss, MC quantile sampling	—
EMQ (Yan et al., 2022)	Boosted multi-quantiles, monotonicity	Ensemble model variance
QRF/RF-GAP (Li et al., 5 Aug 2024)	Proximity-weighted assignment	Tree ensemble variance
β-QR (Akrami et al., 2023)	Robust divergence reweighting	(Built-in)
QXGBoost (Yin et al., 2023)	Huber-smoothed quantile loss	Gradient-boosted ensemble
E-QR (Ansari et al., 18 Dec 2024)	Ensemble pinball loss	Ensemble/model variance
CLEAR (Azizi et al., 10 Jul 2025)	Quantile regression (residuals)	PCS ensemble, conformal cal.
PAC-Bayes (Mai, 3 Sep 2024)	Gibbs exponential weighting (risk-based)	Posterior spread, MCMC

5. Separation of Aleatoric and Epistemic Uncertainty

Accurately separating aleatoric (inherent data noise) and epistemic (model, data, or knowledge deficiency) uncertainty is essential for informed decision-making and risk assessment. Recent frameworks achieve this by:

• Ensemble Quantile Regression (E-QR): Iteratively refines data acquisition and retraining, attributing the residual uncertainty (unreduced by additional data) to the aleatoric component, while the reduction after acquiring more data indicates epistemic uncertainty (Ansari et al., 18 Dec 2024).
• Calibration Frameworks (CLEAR): Fits input-dependent quantile regression to residuals for aleatoric estimation and epistemic estimation via ensembles, combining the two using data-driven calibration:

$$C(x) = [f(x) - \gamma_1 (q^{ale}_{\alpha/2}(x) + \lambda q^{epi}_{\alpha/2}(x)),\ f(x) + \gamma_1 (q^{ale}_{1-\alpha/2}(x) + \lambda q^{epi}_{1-\alpha/2}(x))]$$

Parameters $\gamma_1$ and $\lambda$ are calibrated to minimize quantile loss while maintaining coverage. In empirical studies, this approach reduced interval width by up to 28.2% while maintaining nominal coverage, with $\lambda$ adapting to the dominant uncertainty source (Azizi et al., 10 Jul 2025).

• Uncertainty-Aware Conformal Quantile Regression (UACQR): Constructs intervals that adaptively widen in high-epistemic regions by leveraging variability among quantile estimators, while the baseline width reflects aleatoric uncertainty (Rossellini et al., 2023).

Evidential learning–based deep quantile regression models learn higher-order uncertainty distributions, allowing simultaneous output of both types of uncertainty in a single forward pass (Hüttel et al., 2023).

6. Robustness, Limitations, and Future Directions

Despite its flexibility, quantile regression is subject to practical challenges:

• Under-Coverage in High Dimensions: Theoretical work has revealed that vanilla quantile regression systematically under-covers target probability levels in high-dimensional, low-sample regimes, with coverage $\approx \alpha - (\alpha - 1/2)d/n$ (Bai et al., 2021). Remediation includes conformalization, ensemble calibration, and regularization.
• Robustness to Outlier Features: Standard quantile regression, while robust to label outliers, can be sensitive to outlier covariates. β-divergence approaches and least-trimmed techniques provide increased resilience (Akrami et al., 2023).
• Scalability: Efficient algorithms (e.g., gradient-boosted, ensemble, or proximity-based methods) and MCMC variants for Bayesian quantile regression have been developed to allow large-scale and high-dimensional applications (Yin et al., 2023, Li et al., 5 Aug 2024, Mai, 3 Sep 2024).
• Model Flexibility and Interpretability: Ensemble and flow-based quantile methods offer a principled balance between parametric interpretability and the capacity to represent asymmetric, heavy-tailed, or multimodal uncertainty structures (Yan et al., 2022, Si et al., 2021).

Ongoing work focuses on improved calibration algorithms, finer disaggregation of uncertainty, adaptive acquisition for active learning, efficient variable selection for quantile targets, and tight theoretical guarantees under minimal assumptions. The modularity of quantile regression frameworks, especially in their capacity to integrate with conformal, Bayesian, or ensemble-based extensions, is likely to facilitate further advances in uncertainty quantification for real-world high-stakes domains.

7. Notable Case Studies and Empirical Outcomes

Practical impacts and evaluation results documented include:

• RAQE outperformed transformation-based control limit setting in semiconductors, reducing mean square error in tails from 0.107 (transformation) to 0.012 (RAQE), demonstrating its ability to capture aleatoric variability where it is most consequential (Salazar-Alvarez et al., 2017).
• In a high-dimensional nuclear fusion dataset, model-agnostic quantile regression and explicit calibration losses delivered competitive calibration (expected calibration error, group calibration) and sharper intervals compared to vanilla pinball-loss methods (Chung et al., 2020).
• CLEAR, aggregating quantile regression-based aleatoric estimates and ensemble-based epistemic estimates, consistently achieved improved or comparable conditional coverage with reduced prediction interval width—up to 28.2% narrower versus individual calibrated baselines—across 17 real datasets and adapting to local uncertainty regimes (Azizi et al., 10 Jul 2025).
• In satellite heat reliability studies, Deep MC-QR methods quantified aleatoric uncertainty propagation, informing interval-based Bayesian reliability network analyses under realistic noise, demonstrating operational value for critical engineering systems (Zheng et al., 2022).

These outcomes underscore the versatility and efficacy of quantile regression for modeling aleatoric uncertainty, particularly when equipped with advanced calibration, robustness, and model-aggregation strategies.