A data-driven approach consolidates unstructured disturbances into residual terms estimated from data to yield causal and distributionally consistent stochastic predictors for uncertainty quantification via polynomial chaos expansions and Chebyshev inequalities, validated on Norwegian smart-home实验数据
Distributional uncertainty propagation via optimal transport
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Develops robust SGLD with non-asymptotic convergence bounds for non-convex DRO and applies it to neural network regression under adversarial corruption.
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Uncertainty Propagation under Residual Disturbances: A Smart-Home Case Study
A data-driven approach consolidates unstructured disturbances into residual terms estimated from data to yield causal and distributionally consistent stochastic predictors for uncertainty quantification via polynomial chaos expansions and Chebyshev inequalities, validated on Norwegian smart-home实验数据
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Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems
Develops robust SGLD with non-asymptotic convergence bounds for non-convex DRO and applies it to neural network regression under adversarial corruption.