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Tag: uncertainty

  • ConformalHDC: Uncertainty-Aware Hyperdimensional Computing with Application to Neural Decoding

    ConformalHDC: Uncertainty-Aware Hyperdimensional Computing with Application to Neural Decoding arXiv:2602.21446v1 Announce Type: new Abstract: Hyperdimensional Computing (HDC) offers a computationally efficient paradigm for neuromorphic learning. Yet, it lacks rigorous uncertainty quantification, leading to open decision boundaries and, consequently, vulnerability to outliers, adversarial perturbations, and out-of-distribution inputs. To address these limitations, we introduce ConformalHDC, a unified…

  • Quantifying Epistemic Uncertainty in Diffusion Models

    Quantifying Epistemic Uncertainty in Diffusion Models arXiv:2602.09170v1 Announce Type: new Abstract: To ensure high quality outputs, it is important to quantify the epistemic uncertainty of diffusion models.Existing methods are often unreliable because they mix epistemic and aleatoric uncertainty. We introduce a method based on Fisher information that explicitly isolates epistemic variance, producing more reliable plausibility…

  • Uncertainty-Aware Multimodal Learning via Conformal Shapley Intervals

    Uncertainty-Aware Multimodal Learning via Conformal Shapley Intervals arXiv:2602.00171v1 Announce Type: new Abstract: Multimodal learning combines information from multiple data modalities to improve predictive performance. However, modalities often contribute unequally and in a data dependent way, making it unclear which data modalities are genuinely informative and to what extent their contributions can be trusted. Quantifying modality…

  • Uncertainty Quantification for Large Language Model Reward Learning under Heterogeneous Human Feedback

    Uncertainty Quantification for Large Language Model Reward Learning under Heterogeneous Human Feedback arXiv:2512.03208v1 Announce Type: new Abstract: We study estimation and statistical inference for reward models used in aligning large language models (LLMs). A key component of LLM alignment is reinforcement learning from human feedback (RLHF), where humans compare pairs of model-generated answers and their…

  • Beyond Uncertainty Sets: Leveraging Optimal Transport to Extend Conformal Predictive Distribution to Multivariate Settings

    Beyond Uncertainty Sets: Leveraging Optimal Transport to Extend Conformal Predictive Distribution to Multivariate Settings arXiv:2511.15146v1 Announce Type: new Abstract: Conformal prediction (CP) constructs uncertainty sets for model outputs with finite-sample coverage guarantees. A candidate output is included in the prediction set if its non-conformity score is not considered extreme relative to the scores observed on…

  • Certainty in Uncertainty: Reasoning over Uncertain Knowledge Graphs with Statistical Guarantees

    Certainty in Uncertainty: Reasoning over Uncertain Knowledge Graphs with Statistical Guarantees arXiv:2510.24754v1 Announce Type: new Abstract: Uncertain knowledge graph embedding (UnKGE) methods learn vector representations that capture both structural and uncertainty information to predict scores of unseen triples. However, existing methods produce only point estimates, without quantifying predictive uncertainty-limiting their reliability in high-stakes applications where…

  • Frequentist Validity of Epistemic Uncertainty Estimators

    Frequentist Validity of Epistemic Uncertainty Estimators arXiv:2510.22063v1 Announce Type: new Abstract: Decomposing prediction uncertainty into its aleatoric (irreducible) and epistemic (reducible) components is critical for the development and deployment of machine learning systems. A popular, principled measure for epistemic uncertainty is the mutual information between the response variable and model parameters. However, evaluating this measure…

  • When Robustness Meets Conservativeness: Conformalized Uncertainty Calibration for Balanced Decision Making

    When Robustness Meets Conservativeness: Conformalized Uncertainty Calibration for Balanced Decision Making arXiv:2510.07750v1 Announce Type: new Abstract: Robust optimization safeguards decisions against uncertainty by optimizing against worst-case scenarios, yet their effectiveness hinges on a prespecified robustness level that is often chosen ad hoc, leading to either insufficient protection or overly conservative and costly solutions. Recent approaches…

  • System-Level Uncertainty Quantification with Multiple Machine Learning Models: A Theoretical Framework

    System-Level Uncertainty Quantification with Multiple Machine Learning Models: A Theoretical Framework arXiv:2509.16663v1 Announce Type: new Abstract: ML models have errors when used for predictions. The errors are unknown but can be quantified by model uncertainty. When multiple ML models are trained using the same training points, their model uncertainties may be statistically dependent. In reality,…

  • Uncertainty Estimation using Variance-Gated Distributions

    Uncertainty Estimation using Variance-Gated Distributions arXiv:2509.08846v1 Announce Type: cross Abstract: Evaluation of per-sample uncertainty quantification from neural networks is essential for decision-making involving high-risk applications. A common approach is to use the predictive distribution from Bayesian or approximation models and decompose the corresponding predictive uncertainty into epistemic (model-related) and aleatoric (data-related) components. However, additive decomposition…

  • DICOM De-Identification via Hybrid AI and Rule-Based Framework for Scalable, Uncertainty-Aware Redaction

    DICOM De-Identification via Hybrid AI and Rule-Based Framework for Scalable, Uncertainty-Aware Redaction arXiv:2507.23736v1 Announce Type: new Abstract: Access to medical imaging and associated text data has the potential to drive major advances in healthcare research and patient outcomes. However, the presence of Protected Health Information (PHI) and Personally Identifiable Information (PII) in Digital Imaging and…

  • LVM-GP: Uncertainty-Aware PDE Solver via coupling latent variable model and Gaussian process

    LVM-GP: Uncertainty-Aware PDE Solver via coupling latent variable model and Gaussian process arXiv:2507.22493v1 Announce Type: new Abstract: We propose a novel probabilistic framework, termed LVM-GP, for uncertainty quantification in solving forward and inverse partial differential equations (PDEs) with noisy data. The core idea is to construct a stochastic mapping from the input to a high-dimensional…

  • Detect LLM hallucinations using uncertainty quantification techniques with UQLM

    Detect LLM hallucinations using uncertainty quantification techniques with UQLM UQLM (uncertainty quantification for language models) is an open source Python package for generation time, zero-resource hallucination detection. It leverages state-of-the-art uncertainty quantification (UQ) techniques from the academic literature to compute response-level confidence scores based on response consistency (in multiple responses to the same prompt), token…

  • Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators

    Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators arXiv:2507.11574v1 Announce Type: cross Abstract: Robust uncertainty quantification (UQ) remains a critical barrier to the safe deployment of deep learning in real-time virtual sensing, particularly in high-stakes domains where sparse, noisy, or non-collocated sensor data are the norm. We introduce the Conformalized Monte Carlo Operator (CMCO), a…

  • CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk

    CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk arXiv:2507.08150v1 Announce Type: new Abstract: Accurate uncertainty quantification is critical for reliable predictive modeling, especially in regression tasks. Existing methods typically address either aleatoric uncertainty from measurement noise or epistemic uncertainty from limited data, but not necessarily both in a balanced way. We propose CLEAR, a calibration…

  • Scaling Laws for Uncertainty in Deep Learning

    Scaling Laws for Uncertainty in Deep Learning arXiv:2506.09648v1 Announce Type: new Abstract: Deep learning has recently revealed the existence of scaling laws, demonstrating that model performance follows predictable trends based on dataset and model sizes. Inspired by these findings and fascinating phenomena emerging in the over-parameterized regime, we examine a parallel direction: do similar scaling…

  • Position: There Is No Free Bayesian Uncertainty Quantification

    Position: There Is No Free Bayesian Uncertainty Quantification arXiv:2506.03670v1 Announce Type: new Abstract: Due to their intuitive appeal, Bayesian methods of modeling and uncertainty quantification have become popular in modern machine and deep learning. When providing a prior distribution over the parameter space, it is straightforward to obtain a distribution over the parameters that is…

  • Avoiding Costly Mistakes with Uncertainty Quantification for Algorithmic Home Valuations

    Avoiding Costly Mistakes with Uncertainty Quantification for Algorithmic Home Valuations When you’re about to buy a home, whether you’re an everyday buyer looking for your dream house or a seasoned property investor, there’s a good chance you’ve encountered automated valuation models, or AVMs. These clever tools use massive datasets filled with past property transactions to…

  • ConfEviSurrogate: A Conformalized Evidential Surrogate Model for Uncertainty Quantification

    ConfEviSurrogate: A Conformalized Evidential Surrogate Model for Uncertainty Quantification arXiv:2504.02919v1 Announce Type: new Abstract: Surrogate models, crucial for approximating complex simulation data across sciences, inherently carry uncertainties that range from simulation noise to model prediction errors. Without rigorous uncertainty quantification, predictions become unreliable and hence hinder analysis. While methods like Monte Carlo dropout and ensemble…

  • Uncertainty Quantification in Machine Learning with an Easy Python Interface

    Uncertainty Quantification in Machine Learning with an Easy Python Interface Uncertainty quantification (UQ) in a Machine Learning (ML) model allows one to estimate the precision of its predictions. This is extremely important for utilizing its predictions in real-world tasks. For instance, if a machine learning model is trained to predict a property of a material,…

  • LAPD: Langevin-Assisted Bayesian Active Learning for Physical Discovery

    LAPD: Langevin-Assisted Bayesian Active Learning for Physical Discovery arXiv:2503.02983v1 Announce Type: new Abstract: Discovering physical laws from data is a fundamental challenge in scientific research, particularly when high-quality data are scarce or costly to obtain. Traditional methods for identifying dynamical systems often struggle with noise sensitivity, inefficiency in data usage, and the inability to quantify…

  • Post-Hoc Uncertainty Quantification in Pre-Trained Neural Networks via Activation-Level Gaussian Processes

    Post-Hoc Uncertainty Quantification in Pre-Trained Neural Networks via Activation-Level Gaussian Processes arXiv:2502.20966v1 Announce Type: new Abstract: Uncertainty quantification in neural networks through methods such as Dropout, Bayesian neural networks and Laplace approximations is either prone to underfitting or computationally demanding, rendering these approaches impractical for large-scale datasets. In this work, we address these shortcomings by…

  • Epistemic Uncertainty in Conformal Scores: A Unified Approach

    Epistemic Uncertainty in Conformal Scores: A Unified Approach arXiv:2502.06995v1 Announce Type: new Abstract: Conformal prediction methods create prediction bands with distribution-free guarantees but do not explicitly capture epistemic uncertainty, which can lead to overconfident predictions in data-sparse regions. Although recent conformal scores have been developed to address this limitation, they are typically designed for specific…

  • Uncertainty Quantification with the Empirical Neural Tangent Kernel

    Uncertainty Quantification with the Empirical Neural Tangent Kernel arXiv:2502.02870v1 Announce Type: new Abstract: While neural networks have demonstrated impressive performance across various tasks, accurately quantifying uncertainty in their predictions is essential to ensure their trustworthiness and enable widespread adoption in critical systems. Several Bayesian uncertainty quantification (UQ) methods exist that are either cheap or reliable,…

  • On weight and variance uncertainty in neural networks for regression tasks

    On weight and variance uncertainty in neural networks for regression tasks arXiv:2501.04272v1 Announce Type: new Abstract: We consider the problem of weight uncertainty proposed by [Blundell et al. (2015). Weight uncertainty in neural network. In International conference on machine learning, 1613-1622, PMLR.] in neural networks {(NNs)} specialized for regression tasks. {We further} investigate the effect…

  • Distribution free uncertainty quantification in neuroscience-inspired deep operators

    Distribution free uncertainty quantification in neuroscience-inspired deep operators arXiv:2412.09369v1 Announce Type: new Abstract: Energy-efficient deep learning algorithms are essential for a sustainable future and feasible edge computing setups. Spiking neural networks (SNNs), inspired from neuroscience, are a positive step in the direction of achieving the required energy efficiency. However, in a bid to lower the…

  • Nonlinearity and Uncertainty Informed Moment-Matching Gaussian Mixture Splitting

    Nonlinearity and Uncertainty Informed Moment-Matching Gaussian Mixture Splitting arXiv:2412.00343v1 Announce Type: new Abstract: Many problems in navigation and tracking require increasingly accurate characterizations of the evolution of uncertainty in nonlinear systems. Nonlinear uncertainty propagation approaches based on Gaussian mixture density approximations offer distinct advantages over sampling based methods in their computational cost and continuous representation.…