Power systems optimization under uncertainty: A review of methods and applications
Electric power systems and the companies and customers that interact with them are
experiencing increasing levels of uncertainty due to factors such as renewable energy …
experiencing increasing levels of uncertainty due to factors such as renewable energy …
Frameworks and results in distributionally robust optimization
The concepts of risk aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. The statistical learning community has …
have developed significantly over the last decade. The statistical learning community has …
Towards out-of-distribution generalization: A survey
Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …
test data follow the same statistical pattern, which is mathematically referred to as …
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 …
Certifying some distributional robustness with principled adversarial training
Neural networks are vulnerable to adversarial examples and researchers have proposed
many heuristic attack and defense mechanisms. We address this problem through the …
many heuristic attack and defense mechanisms. We address this problem through the …
Decision-focused learning: Foundations, state of the art, benchmark and future opportunities
Decision-focused learning (DFL) is an emerging paradigm that integrates machine learning
(ML) and constrained optimization to enhance decision quality by training ML models in an …
(ML) and constrained optimization to enhance decision quality by training ML models in an …
Large-scale methods for distributionally robust optimization
We propose and analyze algorithms for distributionally robust optimization of convex losses
with conditional value at risk (CVaR) and $\chi^ 2$ divergence uncertainty sets. We prove …
with conditional value at risk (CVaR) and $\chi^ 2$ divergence uncertainty sets. We prove …
An overview of diffusion models: Applications, guided generation, statistical rates and optimization
Diffusion models, a powerful and universal generative AI technology, have achieved
tremendous success in computer vision, audio, reinforcement learning, and computational …
tremendous success in computer vision, audio, reinforcement learning, and computational …
Learning models with uniform performance via distributionally robust optimization
Learning models with uniform performance via distributionally robust optimization Page 1 The
Annals of Statistics 2021, Vol. 49, No. 3, 1378–1406 https://doi.org/10.1214/20-AOS2004 © …
Annals of Statistics 2021, Vol. 49, No. 3, 1378–1406 https://doi.org/10.1214/20-AOS2004 © …
Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming
This paper reviews recent advances in the field of optimization under uncertainty via a
modern data lens, highlights key research challenges and promise of data-driven …
modern data lens, highlights key research challenges and promise of data-driven …