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 …
Distributionally robust optimization: A review
H Rahimian, S Mehrotra - arXiv preprint arXiv:1908.05659, 2019 - arxiv.org
The concepts of risk-aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. Statistical learning community has also …
have developed significantly over the last decade. Statistical learning community has also …
Edge artificial intelligence for 6G: Vision, enabling technologies, and applications
KB Letaief, Y Shi, J Lu, J Lu - IEEE Journal on Selected Areas …, 2021 - ieeexplore.ieee.org
The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …
wireless networks. It has been envisioned that 6G will be transformative and will …
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 …
Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization
Overparameterized neural networks can be highly accurate on average on an iid test set yet
consistently fail on atypical groups of the data (eg, by learning spurious correlations that …
consistently fail on atypical groups of the data (eg, by learning spurious correlations that …
Computational optimal transport: With applications to data science
Optimal transport (OT) theory can be informally described using the words of the French
mathematician Gaspard Monge (1746–1818): A worker with a shovel in hand has to move a …
mathematician Gaspard Monge (1746–1818): A worker with a shovel in hand has to move a …
Data-enabled predictive control: In the shallows of the DeePC
We consider the problem of optimal trajectory tracking for unknown systems. A novel data-
enabled predictive control (DeePC) algorithm is presented that computes optimal and safe …
enabled predictive control (DeePC) algorithm is presented that computes optimal and safe …
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 …
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 …