Quantile-constrained Wasserstein projections for robust interpretability of numerical and machine learning models
DOI10.1214/24-EJS2268MaRDI QIDQ6595779
B. Iooss, Marouane Il Idrissi, Fabrice Gamboa, N. Bousquet, Jean-Michel Loubes
Publication date: 30 August 2024
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
sensitivity analysisquantilesinterpretabilitymachine learningepistemic uncertaintycomputer modeldomain uncertaintyisotonic polynomialsrobutness
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics in engineering and industry; control charts (62P30) Robustness and adaptive procedures (parametric inference) (62F35) Sensitivity, stability, well-posedness (49K40) Sums of squares and representations by other particular quadratic forms (11E25) Applications of statistics (62P99) Approximation by polynomials (41A10) General topics in artificial intelligence (68T01) Sensitivity analysis for optimization problems on manifolds (49Q12)
Cites Work
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Conic optimization via operator splitting and homogeneous self-dual embedding
- Monge-Kantorovich depth, quantiles, ranks and signs
- A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing
- An introduction to copulas.
- Detecting and modeling critical dependence structures between random inputs of computer models
- Positivity of cubic polynomials on intervals and positive spline interpolation
- I-divergence geometry of probability distributions and minimization problems
- On the construction of optimal monotone cubic spline interpolations
- Improving predictive inference under covariate shift by weighting the log-likelihood function
- UTA-poly and UTA-splines: additive value functions with polynomial marginals
- Copula theory and probabilistic sensitivity analysis: is there a connection?
- Gaussian process modeling with inequality constraints
- Generalized Hoeffding-Sobol decomposition for dependent variables -- application to sensitivity analysis
- Distribution and quantile functions, ranks and signs in dimension \(d\): a measure transportation approach
- Optimal transport for applied mathematicians. Calculus of variations, PDEs, and modeling
- A remark on the optimal transport between two probability measures sharing the same copula
- Nonlinear programming
- Copulas in Machine Learning
- Uncertainty quantification. Theory, implementation, and applications
- An Introduction to Polynomial and Semi-Algebraic Optimization
- Sobol' Indices and Shapley Value
- Correlations and Copulas for Decision and Risk Analysis
- Shapley Effects for Global Sensitivity Analysis: Theory and Computation
- Variance Reduction for Estimation of Shapley Effects and Adaptation to Unknown Input Distribution
- The Impact of Uncertainty Shocks
- Goodness of fit test for isotonic regression
- On Choosing and Bounding Probability Metrics
- Global Sensitivity Analysis and Wasserstein Spaces
- Basics and Trends in Sensitivity Analysis
- Foundations of Modern Probability
- Density modification-based reliability sensitivity analysis
- Fast and flexible methods for monotone polynomial fitting
- Distributional Sensitivity for Uncertainty Quantification
- Mean decrease accuracy for random forests: inconsistency, and a practical solution via the Sobol-MDA
- Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates
- Diffeomorphic Registration Using Sinkhorn Divergences
- The Elements of Statistical Learning
- Explaining machine learning models using entropic variable projection
- Variable importance in regression models
- An adaptive kriging method for solving nonlinear inverse statistical problems
- An Information Geometry Approach to Robustness Analysis for the Uncertainty Quantification of Computer Codes
This page was built for publication: Quantile-constrained Wasserstein projections for robust interpretability of numerical and machine learning models
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6595779)
