Statistical analysis of Wasserstein distributionally robust estimators
J Blanchet, K Murthy… - Tutorials in Operations …, 2021 - pubsonline.informs.org
We consider statistical methods that invoke a min-max distributionally robust formulation to
extract good out-of-sample performance in data-driven optimization and learning problems …
extract good out-of-sample performance in data-driven optimization and learning problems …
Distributionally favorable optimization: A framework for data-driven decision-making with endogenous outliers
A typical data-driven stochastic program seeks the best decision that minimizes the sum of a
deterministic cost function and an expected recourse function under a given distribution …
deterministic cost function and an expected recourse function under a given distribution …
Distributionally robust optimization and robust statistics
We review distributionally robust optimization (DRO), a principled approach for constructing
statistical estimators that hedge against the impact of deviations in the expected loss …
statistical estimators that hedge against the impact of deviations in the expected loss …
On linear optimization over Wasserstein balls
Wasserstein balls, which contain all probability measures within a pre-specified Wasserstein
distance to a reference measure, have recently enjoyed wide popularity in the …
distance to a reference measure, have recently enjoyed wide popularity in the …
Wasserstein Distributionally Robust Linear-Quadratic Estimation under Martingale Constraints
K Lotidis, N Bambos, J Blanchet… - … Conference on Artificial …, 2023 - proceedings.mlr.press
We focus on robust estimation of the unobserved state of a discrete-time stochastic system
with linear dynamics. A standard analysis of this estimation problem assumes a baseline …
with linear dynamics. A standard analysis of this estimation problem assumes a baseline …
Robust bayesian recourse
Algorithmic recourse aims to recommend an informative feedback to overturn an
unfavorable machine learning decision. We introduce in this paper the Bayesian recourse, a …
unfavorable machine learning decision. We introduce in this paper the Bayesian recourse, a …
Sequential domain adaptation by synthesizing distributionally robust experts
Least squares estimators, when trained on few target domain samples, may predict poorly.
Supervised domain adaptation aims to improve the predictive accuracy by exploiting …
Supervised domain adaptation aims to improve the predictive accuracy by exploiting …
[PDF][PDF] Contextual decision-making under parametric uncertainty and data-driven optimistic optimization
We consider decision-making problems with contextual information, in which the reward
function involves uncertain parameters that can be predicted using covariates. To quantify …
function involves uncertain parameters that can be predicted using covariates. To quantify …
Calculating optimistic likelihoods using (geodesically) convex optimization
VA Nguyen, S Shafieezadeh Abadeh… - Advances in …, 2019 - proceedings.neurips.cc
A fundamental problem arising in many areas of machine learning is the evaluation of the
likelihood of a given observation under different nominal distributions. Frequently, these …
likelihood of a given observation under different nominal distributions. Frequently, these …
Robust bayesian classification using an optimistic score ratio
We build a Bayesian contextual classification model using an optimistic score ratio for robust
binary classification when there is limited information on the class-conditional, or contextual …
binary classification when there is limited information on the class-conditional, or contextual …