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Found 12 papers, 3 papers with code
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels
Supervised learning has recently garnered significant attention in the field of computational physics due to its ability to effectively extract complex patterns for tasks like solving partial differential equations, or predicting material properties.
Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization
Stein thinning is a promising algorithm proposed by (Riabiz et al., 2022) for post-processing outputs of Markov chain Monte Carlo (MCMC).
Benchmarking Bayesian neural networks and evaluation metrics for regression tasks
Due to the growing adoption of deep neural networks in many fields of science and engineering, modeling and estimating their uncertainties has become of primary importance.
SHAFF: Fast and consistent SHApley eFfect estimates via random Forests
Interpretability of learning algorithms is crucial for applications involving critical decisions, and variable importance is one of the main interpretation tools.
A sampling criterion for constrained Bayesian optimization with uncertainties
The main contribution of this work is an acquisition criterion that accounts for both the average improvement in objective function and the constraint reliability.
MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA
Variable importance measures are the main tools to analyze the black-box mechanisms of random forests.
Kernel-based ANOVA decomposition and Shapley effects -- Application to global sensitivity analysis
In particular when the inputs are independent, Sobol' sensitivity indices attribute a portion of the output of interest variance to each input and all possible interactions in the model, thanks to a functional ANOVA decomposition.
Statistics Theory Statistics Theory
Interpretable Random Forests via Rule Extraction
We introduce SIRUS (Stable and Interpretable RUle Set) for regression, a stable rule learning algorithm which takes the form of a short and simple list of rules.
Towards new cross-validation-based estimators for Gaussian process regression: efficient adjoint computation of gradients
We consider the problem of estimating the parameters of the covariance function of a Gaussian process by cross-validation.
SIRUS: Stable and Interpretable RUle Set for Classification
State-of-the-art learning algorithms, such as random forests or neural networks, are often qualified as "black-boxes" because of the high number and complexity of operations involved in their prediction mechanism.
Sequential model aggregation for production forecasting
An extension of the deterministic aggregation approach is thus proposed in this paper to provide such multi-step-ahead forecasts.
Global Sensitivity Analysis with Dependence Measures
Global sensitivity analysis with variance-based measures suffers from several theoretical and practical limitations, since they focus only on the variance of the output and handle multivariate variables in a limited way.
