An offline-online decomposition method for efficient linear Bayesian goal-oriented optimal experimental design: Application to optimal sensor placement
Bayesian optimal experimental design (OED) plays an important role in minimizing model
uncertainty with limited experimental data in a Bayesian framework. In many applications …
uncertainty with limited experimental data in a Bayesian framework. In many applications …
Coverage-validity-aware algorithmic recourse
Algorithmic recourse emerges as a prominent technique to promote the explainability,
transparency and hence ethics of machine learning models. Existing algorithmic recourse …
transparency and hence ethics of machine learning models. Existing algorithmic recourse …
An efficient method for goal-oriented linear bayesian optimal experimental design: Application to optimal sensor placemen
Optimal experimental design (OED) plays an important role in the problem of identifying
uncertainty with limited experimental data. In many applications, we seek to minimize the …
uncertainty with limited experimental data. In many applications, we seek to minimize the …
A Geometric Unification of Distributionally Robust Covariance Estimators: Shrinking the Spectrum by Inflating the Ambiguity Set
The state-of-the-art methods for estimating high-dimensional covariance matrices all shrink
the eigenvalues of the sample covariance matrix towards a data-insensitive shrinkage …
the eigenvalues of the sample covariance matrix towards a data-insensitive shrinkage …
On the regularization landscape for the linear recommendation models
Recently, a wide range of recommendation algorithms inspired by deep learning techniques
have emerged as the performance leaders several standard recommendation benchmarks …
have emerged as the performance leaders several standard recommendation benchmarks …
Optimal experimental design for large-scale Bayesian inverse problems
K Wu - 2022 - repositories.lib.utexas.edu
Bayesian optimal experimental design (BOED)—including active learning, Bayesian
optimization, and sensor placement—provides a probabilistic framework to maximize the …
optimization, and sensor placement—provides a probabilistic framework to maximize the …