Uncertainty-Driven Reliability: Selective Prediction and Trustworthy Deployment in Modern Machine Learning

• URL: http://arxiv.org/abs/2508.07556v2
• Date: Sat, 06 Sep 2025 12:35:53 GMT
• Title: Uncertainty-Driven Reliability: Selective Prediction and Trustworthy Deployment in Modern Machine Learning
• Authors: Stephan Rabanser,
• Abstract summary: This thesis investigates how uncertainty estimation can enhance the safety and trustworthiness of machine learning (ML) systems.<n>We first show that a model's training trajectory contains rich uncertainty signals that can be exploited without altering its architecture or loss.<n>We propose a lightweight, post-hoc abstention method that works across tasks, avoids the cost of deep ensembles, and achieves state-of-the-art selective prediction performance.
• Score: 1.2183405753834562
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning (ML) systems are increasingly deployed in high-stakes domains where reliability is paramount. This thesis investigates how uncertainty estimation can enhance the safety and trustworthiness of ML, focusing on selective prediction -- where models abstain when confidence is low. We first show that a model's training trajectory contains rich uncertainty signals that can be exploited without altering its architecture or loss. By ensembling predictions from intermediate checkpoints, we propose a lightweight, post-hoc abstention method that works across tasks, avoids the cost of deep ensembles, and achieves state-of-the-art selective prediction performance. Crucially, this approach is fully compatible with differential privacy (DP), allowing us to study how privacy noise affects uncertainty quality. We find that while many methods degrade under DP, our trajectory-based approach remains robust, and we introduce a framework for isolating the privacy-uncertainty trade-off. Next, we then develop a finite-sample decomposition of the selective classification gap -- the deviation from the oracle accuracy-coverage curve -- identifying five interpretable error sources and clarifying which interventions can close the gap. This explains why calibration alone cannot fix ranking errors, motivating methods that improve uncertainty ordering. Finally, we show that uncertainty signals can be adversarially manipulated to hide errors or deny service while maintaining high accuracy, and we design defenses combining calibration audits with verifiable inference. Together, these contributions advance reliable ML by improving, evaluating, and safeguarding uncertainty estimation, enabling models that not only make accurate predictions -- but also know when to say "I do not know".

Related papers

• Decision-Aware Trust Signal Alignment for SOC Alert Triage [0.0]
  The present paper presents a decision-sensitive trust signal correspondence scheme of SOC alert triage.<n>The framework combines confidence that has been calibrated, lightweight uncertainty cues, and cost-sensitive decision thresholds into coherent decision-support layer.<n>We show that false negatives are greatly amplified by the presence of misaligned displays of confidence, whereas cost weighted loss decreases by orders of magnitude between models with decision aligned trust signals.
  arXiv  Detail & Related papers  (2026-01-08T01:41:54Z)
• 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)
• Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention [65.47632669243657]
  A dishonest institution can exploit mechanisms to discriminate or unjustly deny services under the guise of uncertainty.<n>We demonstrate the practicality of this threat by introducing an uncertainty-inducing attack called Mirage.<n>We propose Confidential Guardian, a framework that analyzes calibration metrics on a reference dataset to detect artificially suppressed confidence.
  arXiv  Detail & Related papers  (2025-05-29T19:47:50Z)
• Probabilistic Modeling of Disparity Uncertainty for Robust and Efficient Stereo Matching [61.73532883992135]
  We propose a new uncertainty-aware stereo matching framework.<n>We adopt Bayes risk as the measurement of uncertainty and use it to separately estimate data and model uncertainty.
  arXiv  Detail & Related papers  (2024-12-24T23:28:20Z)
• Know Where You're Uncertain When Planning with Multimodal Foundation Models: A Formal Framework [54.40508478482667]
  We present a comprehensive framework to disentangle, quantify, and mitigate uncertainty in perception and plan generation.<n>We propose methods tailored to the unique properties of perception and decision-making.<n>We show that our uncertainty disentanglement framework reduces variability by up to 40% and enhances task success rates by 5% compared to baselines.
  arXiv  Detail & Related papers  (2024-11-03T17:32:00Z)
• Beyond Uncertainty Quantification: Learning Uncertainty for Trust-Informed Neural Network Decisions - A Case Study in COVID-19 Classification [7.383605511698832]
  Reliable uncertainty quantification is critical in high-stakes applications, such as medical diagnosis.<n>Traditional uncertainty quantification methods rely on a predefined confidence threshold to classify predictions as confident or uncertain.<n>This approach assumes that predictions exceeding the threshold are trustworthy, while those below it are uncertain, without explicitly assessing the correctness of high-confidence predictions.<n>This study proposes an uncertainty-aware stacked neural network, which extends conventional uncertainty quantification by learning when predictions should be trusted.
  arXiv  Detail & Related papers  (2024-09-19T04:20:12Z)
• Error-Driven Uncertainty Aware Training [7.702016079410588]
  Error-Driven Uncertainty Aware Training aims to enhance the ability of neural classifiers to estimate their uncertainty correctly. The EUAT approach operates during the model's training phase by selectively employing two loss functions depending on whether the training examples are correctly or incorrectly predicted. We evaluate EUAT using diverse neural models and datasets in the image recognition domains considering both non-adversarial and adversarial settings.
  arXiv  Detail & Related papers  (2024-05-02T11:48:14Z)
• Revisiting Confidence Estimation: Towards Reliable Failure Prediction [53.79160907725975]
  We find a general, widely existing but actually-neglected phenomenon that most confidence estimation methods are harmful for detecting misclassification errors. We propose to enlarge the confidence gap by finding flat minima, which yields state-of-the-art failure prediction performance.
  arXiv  Detail & Related papers  (2024-03-05T11:44:14Z)
• Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling [69.83976050879318]
  In large language models (LLMs), identifying sources of uncertainty is an important step toward improving reliability, trustworthiness, and interpretability. In this paper, we introduce an uncertainty decomposition framework for LLMs, called input clarification ensembling. Our approach generates a set of clarifications for the input, feeds them into an LLM, and ensembles the corresponding predictions.
  arXiv  Detail & Related papers  (2023-11-15T05:58:35Z)
• Lightweight, Uncertainty-Aware Conformalized Visual Odometry [2.429910016019183]
  Data-driven visual odometry (VO) is a critical subroutine for autonomous edge robotics. Emerging edge robotics devices like insect-scale drones and surgical robots lack a computationally efficient framework to estimate VO's predictive uncertainties. This paper presents a novel, lightweight, and statistically robust framework that leverages conformal inference (CI) to extract VO's uncertainty bands.
  arXiv  Detail & Related papers  (2023-03-03T20:37:55Z)
• Calibrated Regression Against An Adversary Without Regret [10.470326550507117]
  We introduce online algorithms guaranteed to achieve these goals on arbitrary streams of data points. Specifically, our algorithms produce forecasts that are (1) calibrated -- i.e., an 80% confidence interval contains the true outcome 80% of the time. We implement a post-hoc recalibration strategy that provably achieves these goals in regression.
  arXiv  Detail & Related papers  (2023-02-23T17:42:11Z)
• Reliability-Aware Prediction via Uncertainty Learning for Person Image Retrieval [51.83967175585896]
  UAL aims at providing reliability-aware predictions by considering data uncertainty and model uncertainty simultaneously. Data uncertainty captures the noise" inherent in the sample, while model uncertainty depicts the model's confidence in the sample's prediction.
  arXiv  Detail & Related papers  (2022-10-24T17:53:20Z)

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.