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Robustness studies of black-box models is recognized as a necessary task for numerical models based on structural equations and predictive models learned from data.
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A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing
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Generalized Hoeffding-Sobol decomposition for dependent variables - Application to sensitivity analysis
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A remark on the optimal transport between two probability measures sharing the same copula
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Sobol’ Indices and Shapley Value
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Uncertainty Quantification: Theory, Implementation, and Applications
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Variable importance in regression models
U. Grömping · 2015
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A review on global sensitivity analysis methods
B. Iooss and P. Lemaître · 2015
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Bayesian sensitivity analysis with the Fisher–Rao metric
S. Kurtek and K. Bharath · 2015
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An Introduction to Polynomial and Semi-Algebraic Optimization
J-B. Lasserre · 2015
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Density modification-based reliability sensitivity analysis
P. Lemaître, E. Sergienko, A. Arnaud, N. Bousquet, F. Gamboa, and B. Iooss · 2015
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Sensitivity Analysis for Bayesian Hierarchical Models
M. Roos, T.G. Martins, L. Held, and H. Rue · 2015
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Adversarial Risk via Optimal Transport and Optimal Couplings
M.S. Pydi and V. Jog · 2020
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Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations
Christian A. Scholbeck, Christoph Molnar, Christian Heumann, Bernd Bischl, and Giuseppe Casalicchio · 2020
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Explainability and Fairness in Machine Learning: Improve Fair End-to-end Lending for Kiva
A. Stevens, P. Deruyck, Z. Van Veldhoven, and J. Vanthienen · 2020
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Generalization bounds for deep learning
G. Valle-Pérez and A.A. Louis · 2020
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A Vine Copula-Based Global Sensitivity Analysis Method for Structures with Multidimensional Dependent Variables
Z. Bai, H. Wei, Y. Xiao, S. Song, and S. Kucherenko · 2021
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Optimal Transport for Applied Mathematicians
F. Santambrogio · 2015
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Nonlinear programming
D. P. Bertsekas · 2016
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Fast and flexible methods for monotone polynomial fitting
K. Murray, S. Müller, and B. A. Turlach · 2016
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Conic Optimization via Operator Splitting and Homogeneous Self-Dual Embedding
B. O’Donoghue, E. Chu, N. Parikh, and S. Boyd · 2016
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Shapley Effects for Global Sensitivity Analysis: Theory and Computation
E. Song, B. L. Nelson, and J. Staum · 2016
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Wasserstein generative adversarial networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
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Beyond pinball loss: Quantile methods for calibrated uncertainty quantification
Y. Chung, W. Neiswanger, I. Char, and J. Schneider · 2021
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Basics and Trends in Sensitivity Analysis. Theory and Practice in R
S. Da Veiga, F. Gamboa, B. Iooss, and C. Prieur · 2021
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Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach
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One-step ahead Super Learning from short time series of many slightly dependent data, and anticipating the cost of natural disasters
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Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis
T. Fel, R. Cadene, M. Chalvidal, M. Cord, D. Vigouroux, and T. Serre · 2021
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Global Sensitivity Analysis and Wasserstein Spaces
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Distribution and quantile functions, ranks and signs in dimension d: A measure transportation approach
M. Hallin, E. del Barrio, J. Cuesta-Albertos, and C. Matrán · 2021
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The many faces of robustness: A critical analysis of out-of-distribution generalization
D. Hendrycks, S. Basart, N. Mu, S. Kadavath, F. Wang, E. Dorundo, R. Desai, T. Zhu, S. Parajuli, M. Guo, D. Song, J. Steinhardt, and J. Gilmer · 2021
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Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
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Developments and applications of Shapley effects to reliability-oriented sensitivity analysis with correlated inputs
M. Il Idrissi, V. Chabridon, and B. Iooss · 2021
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Foundations of modern probability
O. Kallenberg · 2021
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Determining the Extinguishing Status of Fuel Flames With Sound Wave by Machine Learning Methods
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Out-of-distribution generalization via risk extrapolation (rex)
D. Krueger, E. Caballero, J.-H. Jacobsen, A. Zhang, J. Binas, D. Zhang, R. Le Priol, and A. Courville · 2021
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Prior knowledge elicitation: The past, present, and future, 2021
P. Mikkola, O. Martin, S. Chandramouli, M. Hartmann, O. Pla, O. Thomas, H. Pesonen, J. Corander, A. Vehtari, S. Kaski, P-C Bürkner, and A. Klami · 2021
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Interpretable Machine Learning. A Guide for Making Black Box Models Explainable
C. Molnar · 2021
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An efficient perturbation approach for multivariate data in sensitive and reliable data mining
M.K. Paul, M.R. Islam, and Sarowar Sattar A.H.M · 2021
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Scenario Weights for Importance Measurement (SWIM) – an R package for sensitivity analysis
S. M. Pesenti, A. Bettini, P. Millossovich, and A. Tsanakas · 2021
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The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support
S. Razavi, A. Jakeman, A. Saltelli, C. Prieur, B. Iooss, E. Borgonovo, E. Plischke, S. Lo Piano, T. Iwanaga, W. Becker, S. Tarantola, J.H.A. Guillaume, J. Jakeman, H. Gupta, N. Melillo, G. Rabitti, V. Chabridon, Q. Duan, X. Sun, S. Smith, R. Sheikholeslami, N. Hosseini, M. Asadzadeh, A. Puy, S. Kucherenko, and H.R. Maier · 2021
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Towards Out-Of-Distribution Generalization: A Survey
Z. Shen, J. Liu, Y. He, X. Zhang, R. Xu, H. Yu, and P. Cui · 2021
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Classification of flame extinction based on acoustic oscillations using artificial intelligence methods
Y. S. Taspinar, M. Koklu, and M. Altin · 2021
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Overparameterization improves robustness to covariate shift in high dimensions
N. Tripuraneni, B. Adlam, and J. Pennington · 2021
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Generalizing to unseen domains: A survey on domain generalization
J. Wang, C. Lan, C. Liu, Y. Ouyang, and T. Qin · 2021
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Deep Learning Generalization and the Convex Hull of Training Sets
R. Yousefzadeh · 2021
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A generalized kernel method for global sensitivity analysis
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