4 papers
Nonparametric Stochastic Subspaces via the Bootstrap for Characterizing Model Error
Akash Yadav, Ruda Zhang
Reliable forward uncertainty quantification in engineering requires methods that account for aleatory and epistemic uncertainties. In many applications, epistemic effects arising f…
Bayesian Optimization under Uncertainty for Training a Scale Parameter in Stochastic Models
Akash Yadav, Ruda Zhang
Hyperparameter tuning is a challenging problem especially when the system itself involves uncertainty. Due to noisy function evaluations, optimization under uncertainty can be comp…
Stochastic Subspace via Probabilistic Principal Component Analysis for Characterizing Model Error
Akash Yadav, Ruda Zhang
This paper proposes a probabilistic model of subspaces based on the probabilistic principal component analysis (PCA). Given a sample of vectors in the embedding space -- commonly k…
Differential Machine Learning for Time Series Prediction
Akash Yadav, Eulalia Nualart
Accurate time series prediction is challenging due to the inherent nonlinearity and sensitivity to initial conditions. We propose a novel approach that enhances neural network pred…