{"list":"other","total":63,"rows":[{"id":"1603.02754","title":"XGBoost: A Scalable Tree Boosting System","created":"2016-03-09","cited_by_count":5166},{"id":"1704.04110","title":"DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks","created":"2017-04-13","cited_by_count":1013},{"id":"1910.09457","title":"Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods","created":"2019-10-21","cited_by_count":913},{"id":"2011.06225","title":"A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges","created":"2020-11-12","cited_by_count":819},{"id":"1404.7828","title":"Deep Learning in Neural Networks: An Overview","created":"2014-04-30","cited_by_count":770},{"id":"2107.03342","title":"A Survey of Uncertainty in Deep Neural Networks","created":"2021-07-07","cited_by_count":680},{"id":"1911.13288","title":"Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019","created":"2019-11-29","cited_by_count":273},{"id":"1909.00590","title":"Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions","created":"2019-09-02","cited_by_count":172},{"id":"1507.04544","title":"Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC","created":"2015-07-16","cited_by_count":110},{"id":"1909.05433","title":"A comparison of some conformal quantile regression methods","created":"2019-09-12","cited_by_count":76},{"id":"1906.04397","title":"Probabilistic Forecasting with Temporal Convolutional Neural Network","created":"2019-06-11","cited_by_count":63},{"id":"1401.3880","title":"Regression Conformal Prediction with Nearest Neighbours","created":"2014-01-16","cited_by_count":59},{"id":"2105.11982","title":"Quantifying Uncertainty in Deep Spatiotemporal Forecasting","created":"2021-05-25","cited_by_count":48},{"id":"1611.05902","title":"Practical heteroskedastic Gaussian process modeling for large simulation experiments","created":"2016-11-17","cited_by_count":39},{"id":"1509.08864","title":"Bayesian model averaging: A systematic review and conceptual classification","created":"2015-09-29","cited_by_count":29},{"id":"2106.01792","title":"Conformal Prediction Bands for Multivariate Functional Data","created":"2021-06-03","cited_by_count":29},{"id":"1503.08195","title":"Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet Representations, and Forecast Rankings","created":"2015-03-27","cited_by_count":28},{"id":"1909.03725","title":"Isotonic Distributional Regression","created":"2019-09-09","cited_by_count":28},{"id":"2002.05777","title":"Semi-Structured Distributional Regression -- Extending Structured Additive Models by Arbitrary Deep Neural Networks and Data Modalities","created":"2020-02-13","cited_by_count":24},{"id":"2111.01968","title":"A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications","created":"2021-11-03","cited_by_count":24},{"id":"2102.00968","title":"CRPS Learning","created":"2021-02-01","cited_by_count":21},{"id":"1508.06749","title":"Most Likely Transformations","created":"2015-08-27","cited_by_count":20},{"id":"1906.02840","title":"Deep Compositional Spatial Models","created":"2019-06-06","cited_by_count":20},{"id":"2106.01682","title":"Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression","created":"2021-06-03","cited_by_count":20},{"id":"1807.11408","title":"Local Linear Forests","created":"2018-07-30","cited_by_count":19},{"id":"2010.03039","title":"Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures","created":"2020-10-06","cited_by_count":19},{"id":"1604.00860","title":"Bayesian Computing with INLA: A Review","created":"2016-04-04","cited_by_count":16},{"id":"1903.06023","title":"Deep Distribution Regression","created":"2019-03-14","cited_by_count":16},{"id":"2005.01181","title":"If You Like It, GAN It. Probabilistic Multivariate Times Series Forecast With GAN","created":"2020-05-03","cited_by_count":14},{"id":"2004.07049","title":"Boosting algorithms in energy research: A systematic review","created":"2020-04-01","cited_by_count":13},{"id":"2104.02705","title":"deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression","created":"2021-04-06","cited_by_count":13},{"id":"2206.08998","title":"A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting","created":"2022-06-17","cited_by_count":12},{"id":"2107.06675","title":"M5 Competition Uncertainty: Overdispersion, distributional forecasting, GAMLSS and beyond","created":"2021-07-14","cited_by_count":11},{"id":"1503.08650","title":"Comparison of Bayesian predictive methods for model selection","created":"2015-03-30","cited_by_count":10},{"id":"1909.04079","title":"Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors","created":"2019-09-09","cited_by_count":10},{"id":"1611.10171","title":"Stability selection for component-wise gradient boosting in multiple dimensions","created":"2016-11-30","cited_by_count":9},{"id":"1807.08429","title":"Prediction based on conditional distributions of vine copulas","created":"2018-07-23","cited_by_count":8},{"id":"2110.08248","title":"Probabilistic Time Series Forecasts with Autoregressive Transformation Models","created":"2021-10-15","cited_by_count":7},{"id":"2202.01657","title":"Deselection of Base-Learners for Statistical Boosting -- with an Application to Distributional Regression","created":"2022-02-03","cited_by_count":7},{"id":"1502.07246","title":"Bayesian Nonparametric Calibration and Combination of Predictive Distributions","created":"2015-02-25","cited_by_count":6},{"id":"1507.00181","title":"Bayesian Additive Regression Trees using Bayesian Model Averaging","created":"2015-07-01","cited_by_count":6},{"id":"1612.06850","title":"Extremal Quantile Regression: An Overview","created":"2016-12-20","cited_by_count":6},{"id":"1908.09482","title":"Marginally-calibrated deep distributional regression","created":"2019-08-26","cited_by_count":6},{"id":"1912.12571","title":"Focused Bayesian Prediction","created":"2019-12-29","cited_by_count":6},{"id":"2006.09219","title":"Distributional (Single) Index Models","created":"2020-06-16","cited_by_count":6},{"id":"1510.04161","title":"D-vine copula based quantile regression","created":"2015-10-14","cited_by_count":5},{"id":"1802.00495","title":"Practical Bayesian Modeling and Inference for Massive Spatial Datasets On Modest Computing Environments","created":"2018-02-01","cited_by_count":5},{"id":"1912.03785","title":"Contrast Trees and Distribution Boosting","created":"2019-12-08","cited_by_count":5},{"id":"2009.13591","title":"Quantile Regression Neural Networks: A Bayesian Approach","created":"2020-09-28","cited_by_count":5},{"id":"2102.04873","title":"Nonparametric C- and D-vine based quantile regression","created":"2021-02-09","cited_by_count":5},{"id":"2103.10739","title":"Recognizing a Spatial Extreme dependence structure: A Deep Learning approach","created":"2021-03-19","cited_by_count":4},{"id":"1312.4584","title":"Statistical Post-Processing of Forecasts for Extremes Using Bivariate Brown-Resnick Processes with an Application to Wind Gusts","created":"2013-12-16","cited_by_count":3},{"id":"1507.03887","title":"An SVM-like Approach for Expectile Regression","created":"2015-07-14","cited_by_count":3},{"id":"1907.04529","title":"Bayesian Inference for Regression Copulas","created":"2019-07-10","cited_by_count":3},{"id":"2001.00412","title":"Circular Regression Trees and Forests with an Application to Probabilistic Wind Direction Forecasting","created":"2020-01-02","cited_by_count":3},{"id":"2102.11309","title":"Bayesian Non-parametric Quantile Process Regression and Estimation of Marginal Quantile Effects","created":"2021-02-22","cited_by_count":3},{"id":"1305.1184","title":"Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal components","created":"2013-05-06","cited_by_count":2},{"id":"1310.8192","title":"spBayes for large univariate and multivariate point-referenced spatio-temporal data models","created":"2013-10-30","cited_by_count":2},{"id":"1712.07750","title":"Approximate Bayesian Forecasting","created":"2017-12-20","cited_by_count":2},{"id":"1910.13119","title":"Joint Quantile Regression for Spatial Data","created":"2019-10-29","cited_by_count":2},{"id":"1702.08185","title":"An update on statistical boosting in biomedicine","created":"2017-02-27","cited_by_count":1},{"id":"1806.03745","title":"Forecast score distributions with imperfect observations","created":"2018-06-10","cited_by_count":1},{"id":"2110.13031","title":"Applying Regression Conformal Prediction with Nearest Neighbors to time series data","created":"2021-10-25","cited_by_count":1}],"next_cursor":null}