Counterfactual explanations (CEs) are a practical tool for demonstrating why machine learning classifiers make particular decisions. For CEs to be useful, it is important that they are easy for users to interpret. Existing methods for generating interpretable CEs rely on auxiliary generative models, which may not be suitable for complex datasets, and incur engineering overhead. We introduce a simple and fast method for generating interpretable CEs in a white-box setting without an auxiliary model, by using the predictive uncertainty of the classifier. Our experiments show that our proposed algorithm generates more interpretable CEs, according to IM1 scores, than existing methods. Additionally, our approach allows us to estimate the uncertainty of a CE, which may be important in safety-critical applications, such as those in the medical domain.
University of Groningen（格罗宁根大学）；Pontifical Catholic University of Peru（秘鲁天主大学）
Neural networks are ubiquitous in many tasks, but trusting their predictions is an open issue. Uncertainty quantification is required for many applications, and disentangled aleatoric and epistemic uncertainties are best. In this paper, we generalize methods to produce disentangled uncertainties to work with different uncertainty quantification methods, and evaluate their capability to produce disentangled uncertainties. Our results show that: there is an interaction between learning aleatoric and epistemic uncertainty, which is unexpected and violates assumptions on aleatoric uncertainty, som…
CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk
Department of Operations, HEC, University of Lausanne（洛桑大学运营管理系）；Department of Statistics, University of California, Berkeley（加州大学伯克利分校统计系）；Department of Electrical Engineering and Computer Science, University of California, Berkeley（加州大学伯克利分校电气工程与计算机科学系）；BegooAI
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；University of Bristol（布里斯托大学）
Typical Bayesian approaches to OOD detection use epistemic uncertainty. Surprisingly from the Bayesian perspective, there are a number of methods that successfully use aleatoric uncertainty to detect OOD points (e.g. Hendryks et al. 2018). In addition, it is difficult to use outlier exposure to improve a Bayesian OOD detection model, as it is not clear whether it is possible or desirable to increase posterior (epistemic) uncertainty at outlier points. We show that a generative model of data curation provides a principled account of aleatoric uncertainty for OOD detection. In particular, aleato…
University of Munich (LMU)（慕尼黑大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Ghent University（根特大学）
Uncertainty quantification has received increasing attention in machine learning in the recent past. In particular, a distinction between aleatoric and epistemic uncertainty has been found useful in this regard. The latter refers to the learner's (lack of) knowledge and appears to be especially difficult to measure and quantify. In this paper, we analyse a recent proposal based on the idea of a second-order learner, which yields predictions in the form of distributions over probability distributions. While standard (first-order) learners can be trained to predict accurate probabilities, namely…
Reducing Aleatoric and Epistemic Uncertainty through Multi-modal Data Acquisition
Université de Lorraine, CentraleSupélec CNRS, LORIA, Metz, France（洛林大学、中央超导电子学学院 CNRS、LORIA、法国梅茨）；Université de technologie de Compiègne UMR CNRS 7253 Heudiasyc, France（图卢兹理工学院 UMR CNRS 7253 Heudiasyc、法国）；CNRS, Université de technologie de Compiègne UMR CNRS 7253 Heudiasyc, France（CNRS、图卢兹理工学院 UMR CNRS 7253 Heudiasyc、法国）；University of Ghent Ghent, Belgium（根特大学、比利时根特）
A Posterior-Predictive Variance Decomposition for Epistemic and Aleatoric Uncertainty in Wind Power Forecasting
School of Engineering, Deakin University（德肯大学工程学院）；ARC Training Centre in Energy Technologies for Future Grids, School of Engineering, University of Wollongong（未来电网能源技术培训中心，沃林戈大学工程学院）
Evaluating deep learning models for fault diagnosis of a rotating machinery with epistemic and aleatoric uncertainty
Faculty of Engineering and Natural Sciences（工程与自然科学学院）；Department of Information and Communications Engineering（信息与通信工程系）；Department of Management, Economics and Industrial Engineering（管理、经济与工业工程系）
CUPID: A Plug-in Framework for Joint Aleatoric and Epistemic Uncertainty Estimation with a Single Model
Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore（南洋理工大学李科钦医学院，新加坡）；College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算与数据科学学院，新加坡）；Centre for Medical Technologies & Innovations, National Health Group, Singapore（国家健康集团医学技术与创新中心，新加坡）
Deep learning techniques have been shown to be extremely effective for various classification and regression problems, but quantifying the uncertainty of their predictions and separating them into the epistemic and aleatoric fractions is still considered challenging. In oil and gas exploration projects, tools consisting of seismic, sonic, magnetic resonance, resistivity, dielectric and/or nuclear sensors are sent downhole through boreholes to probe the earth's rock and fluid properties. The measurements from these tools are used to build reservoir models that are subsequently used for estimati…
Conformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imaging
Department of Electrical and Computer Engineering, University of Rochester（电气与计算机工程系，罗切斯特大学）；Goergen Institute for Data Science & AI, University of Rochester（数据科学与人工智能研究院，罗切斯特大学）
From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence
University of Liverpool（利物浦大学）；University of Edinburgh（爱丁堡大学）；The University of Texas at Dallas（德州大学达拉斯分校）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Simon Fraser University（西蒙菲莎大学）；Kyoto University（京都大学）；Zhejiang University（浙江大学）；National Taiwan Normal University（国立台湾师范大学）；Indiana University（印第安纳大学）；Purdue University（普渡大学）；AppCubic（AppCubic公司）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；The Hong Kong University of Science and Technology（香港科技大学）
HEC Lausanne（洛桑高等商学院）；Facebook AI Research（Facebook人工智能研究院）
We provide single-model estimates of aleatoric and epistemic uncertainty for deep neural networks. To estimate aleatoric uncertainty, we propose Simultaneous Quantile Regression (SQR), a loss function to learn all the conditional quantiles of a given target variable. These quantiles can be used to compute well-calibrated prediction intervals. To estimate epistemic uncertainty, we propose Orthonormal Certificates (OCs), a collection of diverse non-constant functions that map all training samples to zero. These certificates map out-of-distribution examples to non-zero values, signaling epistemic…
University of Munich (LMU)（慕尼黑大学（LMU））；Munich Center for Machine Learning（慕尼黑机器学习中心）；Ghent University（根特大学）
It is well known that accurate probabilistic predictors can be trained through empirical risk minimisation with proper scoring rules as loss functions. While such learners capture so-called aleatoric uncertainty of predictions, various machine learning methods have recently been developed with the goal to let the learner also represent its epistemic uncertainty, i.e., the uncertainty caused by a lack of knowledge and data. An emerging branch of the literature proposes the use of a second-order learner that provides predictions in terms of distributions on probability distributions. However, re…
Paderborn University（帕德博恩大学）；University of Munich (LMU)（慕尼黑大学）
The idea to distinguish and quantify two important types of uncertainty, often referred to as aleatoric and epistemic, has received increasing attention in machine learning research in the last couple of years. In this paper, we consider ensemble-based approaches to uncertainty quantification. Distinguishing between different types of uncertainty-aware learning algorithms, we specifically focus on Bayesian methods and approaches based on so-called credal sets, which naturally suggest themselves from an ensemble learning point of view. For both approaches, we address the question of how to quan…
Supporting model interpretability for complex phenomena where annotators can legitimately disagree, such as emotion recognition, is a challenging machine learning task. In this work, we show that explicitly quantifying the uncertainty in such settings has interpretability benefits. We use a simple modification of a classical network inference using Monte Carlo dropout to give measures of epistemic and aleatoric uncertainty. We identify a significant correlation between aleatoric uncertainty and human annotator disagreement ($r\approx.3$). Additionally, we demonstrate how difficult and subjecti…
Uncertainty Estimation and Out-of-Distribution Detection for LiDAR Scene Semantic Segmentation
Institute of Cartography and Geoinformatics, Leibniz University Hannover（汉诺威莱布尼茨大学制图与地理信息研究所）；Institute of Photogrammetry and GeoInformation, Leibniz University Hannover（汉诺威莱布尼茨大学摄影测量与地理信息研究所）
ETH Zurich（苏黎世联邦理工学院）；Google（谷歌公司）；Technical University Munich（慕尼黑工业大学）
The distribution of a neural network's latent representations has been successfully used to detect out-of-distribution (OOD) data. This work investigates whether this distribution moreover correlates with a model's epistemic uncertainty, thus indicates its ability to generalise to novel inputs. We first empirically verify that epistemic uncertainty can be identified with the surprise, thus the negative log-likelihood, of observing a particular latent representation. Moreover, we demonstrate that the output-conditional distribution of hidden representations also allows quantifying aleatoric unc…
Technical University of Munich（慕尼黑工业大学）；Munich Data Science Institute（慕尼黑数据科学研究所）；Stanford University（斯坦福大学）；Stanford Intelligent Systems Laboratory（斯坦福智能系统实验室）
Characterizing aleatoric and epistemic uncertainty on the predicted rewards can help in building reliable reinforcement learning (RL) systems. Aleatoric uncertainty results from the irreducible environment stochasticity leading to inherently risky states and actions. Epistemic uncertainty results from the limited information accumulated during learning to make informed decisions. Characterizing aleatoric and epistemic uncertainty can be used to speed up learning in a training environment, improve generalization to similar testing environments, and flag unfamiliar behavior in anomalous testing …
German Aerospace Center (DLR)（德国航空航天中心）；Institute for Simulation Intelligence（模拟智能研究所）
There is significant need for principled uncertainty reasoning in machine learning systems as they are increasingly deployed in safety-critical domains. A new approach with uncertainty-aware neural networks (NNs), based on learning evidential distributions for aleatoric and epistemic uncertainties, shows promise over traditional deterministic methods and typical Bayesian NNs, yet several important gaps in the theory and implementation of these networks remain. We discuss three issues with a proposed solution to extract aleatoric and epistemic uncertainties from regression-based neural networks…
Free University of Berlin（柏林自由大学）；RUDN University（俄罗斯人民友谊大学）
We suggest a general approach to quantification of different forms of aleatoric uncertainty in regression tasks performed by artificial neural networks. It is based on the simultaneous training of two neural networks with a joint loss function and a specific hyperparameter $λ>0$ that allows for automatically detecting noisy and clean regions in the input space and controlling their {\em relative contribution} to the loss and its gradients. After the model has been trained, one of the networks performs predictions and the other quantifies the uncertainty of these predictions by estimating the l…
Due to the steadily increasing relevance of machine learning for practical applications, many of which are coming with safety requirements, the notion of uncertainty has received increasing attention in machine learning research in the last couple of years. In particular, the idea of distinguishing between two important types of uncertainty, often refereed to as aleatoric and epistemic, has recently been studied in the setting of supervised learning. In this paper, we propose to quantify these uncertainties with random forests. More specifically, we show how two general approaches for measurin…
HybridFlow: Quantification of Aleatoric and Epistemic Uncertainty with a Single Hybrid Model
Department of Earth, Environment, and Planetary Sciences, Brown University（布朗大学地球、环境与行星科学系）；Data Science Institute, Brown University（布朗大学数据科学研究院）；Department of Computer Science, Brown University（布朗大学计算机科学系）
University of Colorado Boulder（科罗拉多大学博尔德分校）；The Charles Stark Draper Laboratory, Inc.（查尔斯·斯塔克·德雷珀实验室公司）
Giving autonomous agents the ability to forecast their own outcomes and uncertainty will allow them to communicate their competencies and be used more safely. We accomplish this by using a learned world model of the agent system to forecast full agent trajectories over long time horizons. Real world systems involve significant sources of both aleatoric and epistemic uncertainty that compound and interact over time in the trajectory forecasts. We develop a deep generative world model that quantifies aleatoric uncertainty while incorporating the effects of epistemic uncertainty during the learni…
Variational LSTM with Augmented Inputs: Nonlinear Response History Metamodeling with Aleatoric and Epistemic Uncertainty
Department of Civil, Environmental, and Construction Engineering, Texas Tech University（德克萨斯理工大学土木、环境与建筑工程系）；Department of Civil and Environmental Engineering, Rensselaer Polytechnic Institute（伦斯勒理工学院土木与环境工程系）；National Wind Institute, Texas Tech University（德克萨斯理工大学国家风能研究所）
Cooperative Variance Estimation and Bayesian Neural Networks for Disentangling Aleatoric and Epistemic Uncertainties
Faculty of Mechanical Engineering, Delft University of Technology, Mekelweg 2, Delft, 2628 CD, The Netherlands（代尔夫特理工大学机械工程学院）；School of Engineering, Brown University, 184 Hope St., Providence, RI 02912, USA（布朗大学工程学院）
The notion of uncertainty is of major importance in machine learning and constitutes a key element of machine learning methodology. In line with the statistical tradition, uncertainty has long been perceived as almost synonymous with standard probability and probabilistic predictions. Yet, due to the steadily increasing relevance of machine learning for practical applications and related issues such as safety requirements, new problems and challenges have recently been identified by machine learning scholars, and these problems may call for new methodological developments. In particular, this …
