The ability of a deep learning network to distinguish between in-distribution (ID) and out-of-distribution (OOD) inputs is crucial for ensuring the reliability and trustworthiness of AI systems. Existing OOD detection methods often involve complex architectural innovations, such as ensemble models, which, while enhancing detection accuracy, significantly increase model complexity and training time. Other methods utilize surrogate samples to simulate OOD inputs, but these may not generalize well across different types of OOD data. In this paper, we propose a straightforward yet novel technique to enhance OOD detection in pre-trained networks without altering its original parameters. Our approach defines probabilistic trust intervals for each network weight, determined using in-distribution data. During inference, additional weight values are sampled, and the resulting disagreements among outputs are utilized for OOD detection. We propose a metric to quantify this disagreement and validate its effectiveness with empirical evidence. Our method significantly outperforms various baseline methods across multiple OOD datasets without requiring actual or surrogate OOD samples. We evaluate our approach on MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100 and CIFAR-10-C (a corruption-augmented version of CIFAR-10), across various neural network architectures (e.g., VGG-16, ResNet-20, DenseNet-100). On the MNIST-FashionMNIST setup, our method achieves a False Positive Rate (FPR) of 12.46\% at 95\% True Positive Rate (TPR), compared to 27.09\% achieved by the best baseline. On adversarial and corrupted datasets such as CIFAR-10-C, our proposed method easily differentiate between clean and noisy inputs. These results demonstrate the robustness of our approach in identifying corrupted and adversarial inputs, all without requiring OOD samples during training.
Towards Trustworthy Vital Sign Forecasting: Leveraging Uncertainty for Prediction Intervals
College of Computing（计算学院）；Data Science Nanyang Technological University（南洋理工大学数据科学系）；Nanyang Technological University（南洋理工大学）；A*STAR Centre for Frontier AI Research（前沿人工智能研究中心）；School of Computing（计算学院）；University of Utah（犹他大学）；Swee Hock School of Public Health（公共卫生学院）；National University of Singapore（新加坡国立大学）；Lee Kong Chian School of Medicine（李科钦医学院）
Probabilistic electrical power demand forecasting with uncertainty quantification
Nepal College of Information Technology（尼泊尔信息技术学院）；Nepal Telecommunications Authority（尼泊尔电信管理局）；Nepal Electricity Authority（尼泊尔电力局）；University of Bergen（卑尔根大学）；Western Norway University of Applied Sciences（挪威西部应用科学大学）
Accurate quantification of model uncertainty has long been recognized as a fundamental requirement for trusted AI. In regression tasks, uncertainty is typically quantified using prediction intervals calibrated to a specific operating point, making evaluation and comparison across different studies difficult. Our work leverages: (1) the concept of operating characteristics curves and (2) the notion of a gain over a simple reference, to derive a novel operating point agnostic assessment methodology for prediction intervals. The paper describes the corresponding algorithm, provides a theoretical …
Uncertainty quantification is crucial for building reliable and trustable machine learning systems. We propose to estimate uncertainty in recurrent neural networks (RNNs) via stochastic discrete state transitions over recurrent timesteps. The uncertainty of the model can be quantified by running a prediction several times, each time sampling from the recurrent state transition distribution, leading to potentially different results if the model is uncertain. Alongside uncertainty quantification, our proposed method offers several advantages in different settings. The proposed method can (1) lea…
The regulatory approval and broad clinical deployment of medical AI have been hampered by the perception that deep learning models fail in unpredictable and possibly catastrophic ways. A lack of statistically rigorous uncertainty quantification is a significant factor undermining trust in AI results. Recent developments in distribution-free uncertainty quantification present practical solutions for these issues by providing reliability guarantees for black-box models on arbitrary data distributions as formally valid finite-sample prediction intervals. Our work applies these new uncertainty qua…
Probabilistic Machine Learning for Noisy Labels in Earth Observation
Orion Lab（猎户座实验室）；National Observatory of Athens（雅典国家天文台）；National Technical University of Athens（雅典国立技术大学）；Image Processing Laboratory (IPL), Universitat de València（瓦伦西亚大学图像处理实验室）；Harokopio University of Athens（哈罗科皮奥大学）；Archimedes, Athena Research Center（雅典娜研究中心阿基米德分部）
Massachusetts Institute of Technology（麻省理工学院）；Columbia University（哥伦比亚大学）；The London School of Economics and Political Science（伦敦政治经济学院）；University of Kentucky（肯塔基大学）
Probabilistic machine learning increasingly informs critical decisions in medicine, economics, politics, and beyond. We need evidence to support that the resulting decisions are well-founded. To aid development of trust in these decisions, we develop a taxonomy delineating where trust in an analysis can break down: (1) in the translation of real-world goals to goals on a particular set of available training data, (2) in the translation of abstract goals on the training data to a concrete mathematical problem, (3) in the use of an algorithm to solve the stated mathematical problem, and (4) in t…
Improving understanding and trust in AI: How users benefit from interval-based counterfactual explanations
Department of Bioengineering, Imperial College London（伦敦帝国理工学院生物工程系）；Institute of Artificial and Human Intelligence, University of Bayreuth（拜罗伊特大学人工智能与人类智能研究所）
The concept of trustworthy AI has gained widespread attention lately. One of the aspects relevant to trustworthy AI is robustness of ML models. In this study, we show how to probabilistically quantify robustness against naturally occurring distortions of input data for tree-based classifiers under the assumption that the natural distortions can be described by multivariate probability distributions that can be transformed to multivariate normal distributions. The idea is to extract the decision rules of a trained tree-based classifier, separate the feature space into non-overlapping regions an…
Time series data is prevalent in a wide variety of real-world applications and it calls for trustworthy and explainable models for people to understand and fully trust decisions made by AI solutions. We consider the problem of building explainable classifiers from multi-variate time series data. A key criterion to understand such predictive models involves elucidating and quantifying the contribution of time varying input variables to the classification. Hence, we introduce a novel, modular, convolution-based feature extraction and attention mechanism that simultaneously identifies the variabl…
Bayesian structure learning allows one to capture uncertainty over the causal directed acyclic graph (DAG) responsible for generating given data. In this work, we present Tractable Uncertainty for STructure learning (TRUST), a framework for approximate posterior inference that relies on probabilistic circuits as the representation of our posterior belief. In contrast to sample-based posterior approximations, our representation can capture a much richer space of DAGs, while also being able to tractably reason about the uncertainty through a range of useful inference queries. We empirically show…
Forschungszentrum Jülich GmbH（于利希研究中心）；RWTH Aachen University（亚琛工业大学）；University of Cologne（科隆大学）；JARA-ENERGY（于利希-亚琛研究联盟能源部）
Electricity is traded on various markets with different time horizons and regulations. Short-term intraday trading becomes increasingly important due to the higher penetration of renewables. In Germany, the intraday electricity price typically fluctuates around the day-ahead price of the European Power EXchange (EPEX) spot markets in a distinct hourly pattern. This work proposes a probabilistic modeling approach that models the intraday price difference to the day-ahead contracts. The model captures the emerging hourly pattern by considering the four 15 min intervals in each day-ahead price in…
CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）；University of Oxford（牛津大学）
We propose a new method to approximate the posterior distribution of probabilistic programs by means of computing guaranteed bounds. The starting point of our work is an interval-based trace semantics for a recursive, higher-order probabilistic programming language with continuous distributions. Taking the form of (super-/subadditive) measures, these lower/upper bounds are non-stochastic and provably correct: using the semantics, we prove that the actual posterior of a given program is sandwiched between the lower and upper bounds (soundness); moreover the bounds converge to the posterior (com…
Time-dependent Probabilistic Generative Models for Disease Progression
BCAM-Basque Center for Applied Mathematics（巴斯克应用数学中心）；Hospital Universitario Cruces（克鲁塞斯大学医院）；University of the Basque Country UPV/EHU（巴斯克大学）；Osakidetza（奥萨基德萨）
