Among the many ways of quantifying uncertainty in a regression setting, specifying the full quantile function is attractive, as quantiles are amenable to interpretation and evaluation. A model that predicts the true conditional quantiles for each input, at all quantile levels, presents a correct and efficient representation of the underlying uncertainty. To achieve this, many current quantile-based methods focus on optimizing the so-called pinball loss. However, this loss restricts the scope of applicable regression models, limits the ability to target many desirable properties (e.g. calibration, sharpness, centered intervals), and may produce poor conditional quantiles. In this work, we develop new quantile methods that address these shortcomings. In particular, we propose methods that can apply to any class of regression model, allow for selecting a trade-off between calibration and sharpness, optimize for calibration of centered intervals, and produce more accurate conditional quantiles. We provide a thorough experimental evaluation of our methods, which includes a high dimensional uncertainty quantification task in nuclear fusion.
Indian Institute of Technology Delhi（德里印度理工学院）；South Asian University（南亚大学）
In this paper, we propose a novel asymmetric $ε$-insensitive pinball loss function for quantile estimation. There exists some pinball loss functions which attempt to incorporate the $ε$-insensitive zone approach in it but, they fail to extend the $ε$-insensitive approach for quantile estimation in true sense. The proposed asymmetric $ε$-insensitive pinball loss function can make an asymmetric $ε$- insensitive zone of fixed width around the data and divide it using $τ$ value for the estimation of the $τ$th quantile. The use of the proposed asymmetric $ε$-insensitive pinball loss function in Sup…
SEMF: Supervised Expectation-Maximization Framework for Predicting Intervals
HEC, University of Lausanne, Lausanne, Switzerland（洛桑大学HEC学院，洛桑，瑞士）；HEC, University of Lausanne, Expertise Center for Climate Extremes (ECCE), Lausanne, Switzerland（洛桑大学HEC学院，洛桑，瑞士）
E-QRGMM: Efficient Generative Metamodeling for Covariate-Dependent Uncertainty Quantification
School of Data Science（数据科学学院）；School of Management（管理学院）；Department of Decision Analytics and Operations, College of Business, City University of Hong Kong（商业学院决策分析与运营系，香港城市大学）；The Laboratory for AI-Powered Financial Technologies（人工智能赋能金融技术实验室）
The University of Sydney（悉尼大学）；Sydney Institute for Astronomy（悉尼天文研究所）
Although ubiquitous in the sciences, histogram data have not received much attention by the Deep Learning community. Whilst regression and classification tasks for scalar and vector data are routinely solved by neural networks, a principled approach for estimating histogram labels as a function of an input vector or image is lacking in the literature. We present a dedicated method for Deep Learning-based histogram regression, which incorporates cross-bin information and yields distributions over possible histograms, expressed by $τ$-quantiles of the cumulative histogram in each bin. The crux o…
Isotonic Quantile Regression Averaging for uncertainty quantification of electricity price forecasts
Doctoral School, Faculty of Management, Wrocław University of Science and Technology（沃斯克拉大学管理学院博士学院）；Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology（沃斯克拉大学运筹学与商业智能系）
QDeepGR4J: Quantile-based ensemble of deep learning and GR4J hybrid rainfall-runoff models for extreme flow prediction with uncertainty quantification
School of Mathematics and Statistics, University of New South Wales, Sydney, NSW 2052, Australia（数学与统计学学院，新南威尔士大学，悉尼，新南威尔士州，2052，澳大利亚）；Data Analytics for Resources and Environments, Australian Research Council—Industrial Transformation Training Centre, Sydney, NSW, Australia（资源与环境数据分析，澳大利亚研究理事会—工业转型培训中心，悉尼，新南威尔士州，澳大利亚）
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift
Ecole Polytechnique（巴黎综合理工学院）；Lagrange Mathematics and Computing Research Center（拉格朗日数学与计算研究中心）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Technology Innovation Institute（技术创新研究院）
A Unified Perspective on Conformal Prediction and Wasserstein Distributionally Robust Optimization for Uncertainty Quantification
Contextual Robotics Institute, University of California San Diego（加州大学圣迭戈分校语境机器人研究所）；University of Southern California（南加州大学）；California Institute of Technology（加州理工学院）；ETH Zürich（苏黎世联邦理工学院）
Error-quantified Conformal Inference for Time Series
School of Statistics and Data Science, LPMC, KLMDASR and LEBPS, Nankai University（统计与数据科学学院，LPMC，KLMDASR和LEBPS，南开大学）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院，清华大学）
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Colorado School of Mines（科罗拉多矿业学院）；Michigan State University（密歇根州立大学）
Monitoring changes inside a reservoir in real time is crucial for the success of CO2 injection and long-term storage. Machine learning (ML) is well-suited for real-time CO2 monitoring because of its computational efficiency. However, most existing applications of ML yield only one prediction (i.e., the expectation) for a given input, which may not properly reflect the distribution of the testing data, if it has a shift with respect to that of the training data. The Simultaneous Quantile Regression (SQR) method can estimate the entire conditional distribution of the target variable of a neural …
We investigate different methods for regularizing quantile regression when predicting either a subset of quantiles or the full inverse CDF. We show that minimizing an expected pinball loss over a continuous distribution of quantiles is a good regularizer even when only predicting a specific quantile. For predicting multiple quantiles, we propose achieving the classic goal of non-crossing quantiles by using deep lattice networks that treat the quantile as a monotonic input feature, and we discuss why monotonicity on other features is an apt regularizer for quantile regression. We show that latt…
Neural Conditional Probability for Uncertainty Quantification
CSML, Istituto Italiano di Tecnologia（CSML，意大利技术研究院）；University of Novi Sad（诺维萨大学）；CMAP-Ecole Polytechnique（CMAP-巴黎高等学院）；AI Centre, University College London（伦敦大学学院人工智能中心）
Bayesian Transformer for Probabilistic Load Forecasting in Smart Grids
O&M Analytics, AES Clean Energ（O&M分析部，AES清洁能源）；The AES Corporation（AES公司）；Department of Information and Communication Engineering（信息与通信工程系）；Pabna University of Science and Technology（帕纳大学科学与技术）
Analyzing Uncertainty Quantification in Statistical and Deep Learning Models for Probabilistic Electricity Price Forecasting
Institute of Energy Engineering and Energy Economics, Ulm University of Applied Sciences（能源工程与经济学研究所，乌尔姆应用科学大学）；Faculty of Mathematics and Economics, Ulm University（数学与经济学学院，乌尔姆大学）；Faculty of Statistics, TU Dortmund University（统计学院，多特蒙德技术大学）
Bayesian Uncertainty Quantification with Anchored Ensembles for Robust EV Power Consumption Prediction
Connected & Automated Vehicles, National Research Council Canada（连接与自动化车辆，加拿大国家研究委员会）；National Research Council Canada（加拿大国家研究委员会）；Transportation Emissions & Electrification Laboratory（交通运输排放与电动化实验室）；Environment and Climate Change Canada（加拿大环境与气候变化部）
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
e:fs TechHub GmbH（e:fs科技中心有限公司）；Ruhr West University of Applied Sciences（鲁尔西区应用科学大学）
Reliable spatial uncertainty evaluation of object detection models is of special interest and has been subject of recent work. In this work, we review the existing definitions for uncertainty calibration of probabilistic regression tasks. We inspect the calibration properties of common detection networks and extend state-of-the-art recalibration methods. Our methods use a Gaussian process (GP) recalibration scheme that yields parametric distributions as output (e.g. Gaussian or Cauchy). The usage of GP recalibration allows for a local (conditional) uncertainty calibration by capturing dependen…
Uncertainty Quantification of the Virial Black Hole Mass with Conformal Prediction
ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D)（三维全天天体物理ARC卓越中心（ASTRO 3D））；Macquarie University（麦考瑞大学）；CSIRO（澳大利亚联邦科学与工业研究组织）；The Australian National University（澳大利亚国立大学）
