[HTML][HTML] Distributionally robust optimization: A review on theory and applications
In this paper, we survey the primary research on the theory and applications of
distributionally robust optimization (DRO). We start with reviewing the modeling power and …
distributionally robust optimization (DRO). We start with reviewing the modeling power and …
Wasserstein distributionally robust optimization: Theory and applications in machine learning
Many decision problems in science, engineering, and economics are affected by uncertain
parameters whose distribution is only indirectly observable through samples. The goal of …
parameters whose distribution is only indirectly observable through samples. The goal of …
Finite-sample guarantees for Wasserstein distributionally robust optimization: Breaking the curse of dimensionality
R Gao - Operations Research, 2023 - pubsonline.informs.org
Wasserstein distributionally robust optimization (DRO) aims to find robust and generalizable
solutions by hedging against data perturbations in Wasserstein distance. Despite its recent …
solutions by hedging against data perturbations in Wasserstein distance. Despite its recent …
Distributionally robust linear quadratic control
Abstract Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is
studied in various fields such as engineering, computer science, economics, and …
studied in various fields such as engineering, computer science, economics, and …
Distributionally robust learning
R Chen, IC Paschalidis - Foundations and Trends® in …, 2020 - nowpublishers.com
This monograph develops a comprehensive statistical learning framework that is robust to
(distributional) perturbations in the data using Distributionally Robust Optimization (DRO) …
(distributional) perturbations in the data using Distributionally Robust Optimization (DRO) …
FLPK-BiSeNet: Federated learning based on priori knowledge and bilateral segmentation network for image edge extraction
L Teng, Y Qiao, M Shafiq, G Srivastava… - … on Network and …, 2023 - ieeexplore.ieee.org
Federated learning can effectively ensure data security and improve the problem of data
islanding. However, the performance of federated learning-based schemes could be better …
islanding. However, the performance of federated learning-based schemes could be better …
The implicit regularization of stochastic gradient flow for least squares
We study the implicit regularization of mini-batch stochastic gradient descent, when applied
to the fundamental problem of least squares regression. We leverage a continuous-time …
to the fundamental problem of least squares regression. We leverage a continuous-time …
Outlier-robust wasserstein dro
Distributionally robust optimization (DRO) is an effective approach for data-driven decision-
making in the presence of uncertainty. Geometric uncertainty due to~ sampling or localized …
making in the presence of uncertainty. Geometric uncertainty due to~ sampling or localized …
Minimax robust detection: Classic results and recent advances
This paper provides an overview of results and concepts in minimax robust hypothesis
testing for two and multiple hypotheses. It starts with an introduction to the subject …
testing for two and multiple hypotheses. It starts with an introduction to the subject …
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 …