Calibrated Decomposition of Aleatoric and Epistemic Uncertainty in Deep Features for Inference-Time Adaptation

• URL: http://arxiv.org/abs/2511.12389v1
• Date: Sat, 15 Nov 2025 23:47:30 GMT
• Title: Calibrated Decomposition of Aleatoric and Epistemic Uncertainty in Deep Features for Inference-Time Adaptation
• Authors: Divake Kumar, Patrick Poggi, Sina Tayebati, Devashri Naik, Nilesh Ahuja, Amit Ranjan Trivedi,
• Abstract summary: Most estimators collapse all uncertainty modes into a single confidence score, preventing reliable reasoning about when to allocate more compute or adjust inference.<n>We introduce Uncertainty-Guided Inference-Time Selection, a lightweight inference time framework that disentangles aleatoric (data-driven) and model-driven uncertainty directly in deep feature space.
• Score: 3.018583625592182
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most estimators collapse all uncertainty modes into a single confidence score, preventing reliable reasoning about when to allocate more compute or adjust inference. We introduce Uncertainty-Guided Inference-Time Selection, a lightweight inference time framework that disentangles aleatoric (data-driven) and epistemic (model-driven) uncertainty directly in deep feature space. Aleatoric uncertainty is estimated using a regularized global density model, while epistemic uncertainty is formed from three complementary components that capture local support deficiency, manifold spectral collapse, and cross-layer feature inconsistency. These components are empirically orthogonal and require no sampling, no ensembling, and no additional forward passes. We integrate the decomposed uncertainty into a distribution free conformal calibration procedure that yields significantly tighter prediction intervals at matched coverage. Using these components for uncertainty guided adaptive model selection reduces compute by approximately 60 percent on MOT17 with negligible accuracy loss, enabling practical self regulating visual inference. Additionally, our ablation results show that the proposed orthogonal uncertainty decomposition consistently yields higher computational savings across all MOT17 sequences, improving margins by 13.6 percentage points over the total-uncertainty baseline.

Related papers

• Direct Learning of Calibration-Aware Uncertainty for Neural PDE Surrogates [0.0]
  Cross-regularized uncertainty learns uncertainty parameters during training using routed through a held-out regularization split.<n>The predictor is optimized on the training split for fit, while low-dimensional uncertainty controls are optimized on the regularization split to reduce train-test mismatch.<n>We instantiate the approach in Fourier Neural Operators and evaluate on APEBench sweeps over observed fraction and training-set size.
  arXiv  Detail & Related papers  (2026-02-11T17:57:20Z)
• Calibrating Uncertainty for Zero-Shot Adversarial CLIP [33.707647228637114]
  We propose a novel adversarial fine-tuning objective for CLIP considering both prediction accuracy and uncertainty alignments.<n>Our objective aligns these distributions holistically under perturbations, moving beyond single-logit anchoring and restoring uncertainty.
  arXiv  Detail & Related papers  (2025-12-15T05:41:08Z)
• Adaptive Individual Uncertainty under Out-Of-Distribution Shift with Expert-Routed Conformal Prediction [8.306260695214972]
  We introduce Trustworthy Expert Split-conformal with Scaled Estimation for Efficient Reliable Adaptive intervals (TESSERA)<n>TESSERA provides per-sample uncertainty with reliable coverage guarantee, informative and adaptive prediction interval widths that track the absolute error.<n>We evaluate on protein-ligand binding affinity prediction under both independent and identically distributed (i.i.d.) and scaffold-based out-of-distribution (OOD) splits.
  arXiv  Detail & Related papers  (2025-10-17T01:51:33Z)
• Adaptive Dual Uncertainty Optimization: Boosting Monocular 3D Object Detection under Test-Time Shifts [80.32933059529135]
  Test-Time Adaptation (TTA) methods have emerged to adapt to target distributions during inference.<n>We propose Dual Uncertainty Optimization (DUO), the first TTA framework designed to jointly minimize both uncertainties for robust M3OD.<n>In parallel, we design a semantic-aware normal field constraint that preserves geometric coherence in regions with clear semantic cues.
  arXiv  Detail & Related papers  (2025-08-28T07:09:21Z)
• CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk [5.758150962743421]
  We propose CLEAR, a calibration method with two distinct parameters.<n>We show how it can be used with (i) quantile regression for aleatoric uncertainty and (ii) ensembles drawn from the Predictability-Computability-Stability framework.<n>Across 17 diverse real-world datasets, CLEAR achieves an average improvement of 28.2% and 17.4% in the interval width.
  arXiv  Detail & Related papers  (2025-07-10T20:13:00Z)
• SConU: Selective Conformal Uncertainty in Large Language Models [59.25881667640868]
  We propose a novel approach termed Selective Conformal Uncertainty (SConU)<n>We develop two conformal p-values that are instrumental in determining whether a given sample deviates from the uncertainty distribution of the calibration set at a specific manageable risk level.<n>Our approach not only facilitates rigorous management of miscoverage rates across both single-domain and interdisciplinary contexts, but also enhances the efficiency of predictions.
  arXiv  Detail & Related papers  (2025-04-19T03:01:45Z)
• Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learning [0.19418036471925312]
  We introduce a novel framework for generalized Gaussian error modeling in deep reinforcement learning.<n>We improve the estimation and mitigation of data-dependent aleatoric uncertainty.<n> Experiments with policy gradient algorithms demonstrate significant performance gains.
  arXiv  Detail & Related papers  (2024-08-05T08:12:25Z)
• Model-Based Uncertainty in Value Functions [89.31922008981735]
  We focus on characterizing the variance over values induced by a distribution over MDPs. Previous work upper bounds the posterior variance over values by solving a so-called uncertainty Bellman equation. We propose a new uncertainty Bellman equation whose solution converges to the true posterior variance over values.
  arXiv  Detail & Related papers  (2023-02-24T09:18:27Z)
• Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model [68.34559610536614]
  We argue that the aleatoric uncertainty is an inherent attribute of the data and can only be correctly estimated with an unbiased oracle model. We propose a new sampling and selection strategy at train time to approximate the oracle model for aleatoric uncertainty estimation. Our results show that our solution achieves both accurate deterministic results and reliable uncertainty estimation.
  arXiv  Detail & Related papers  (2021-11-22T08:54:10Z)
• Localization Uncertainty Estimation for Anchor-Free Object Detection [48.931731695431374]
  There are several limitations of the existing uncertainty estimation methods for anchor-based object detection. We propose a new localization uncertainty estimation method called UAD for anchor-free object detection. Our method captures the uncertainty in four directions of box offsets that are homogeneous, so that it can tell which direction is uncertain.
  arXiv  Detail & Related papers  (2020-06-28T13:49:30Z)
• Towards Better Performance and More Explainable Uncertainty for 3D Object Detection of Autonomous Vehicles [33.0319422469465]
  We propose a novel form of the loss function to increase the performance of LiDAR-based 3d object detection. With the new loss function, the performance of our method on the val split of KITTI dataset shows up to a 15% increase in terms of Average Precision.
  arXiv  Detail & Related papers  (2020-06-22T05:49:58Z)
• Distribution-free binary classification: prediction sets, confidence intervals and calibration [106.50279469344937]
  We study three notions of uncertainty quantification -- calibration, confidence intervals and prediction sets -- for binary classification in the distribution-free setting. We derive confidence intervals for binned probabilities for both fixed-width and uniform-mass binning. As a consequence of our 'tripod' theorems, these confidence intervals for binned probabilities lead to distribution-free calibration.
  arXiv  Detail & Related papers  (2020-06-18T14:17:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.