Results 31 to 40 of about 6,180 (210)

A Wasserstein-based distributionally robust neural network for non-intrusive load monitoring

Frontiers in Energy Research, 2023
Non-intrusive load monitoring (NILM) is a technique that uses electrical data analysis to disaggregate the total energy consumption of a building or home into the energy consumption of individual appliances. To address the data uncertainty problem in non-
Qing Zhang, Qing Zhang, Yi Yan, Fannie Kong, Shifei Chen, Linfeng Yang, Linfeng Yang   +6 more
doaj   +1 more source

Data-driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulations

, 2017
We consider stochastic programs where the distribution of the uncertain parameters is only observable through a finite training dataset. Using the Wasserstein metric, we construct a ball in the space of (multivariate and non-discrete) probability ...
Esfahani, Peyman Mohajerin, Kuhn, Daniel
core   +1 more source

Distributionally robust optimization

Acta Numerica
Distributionally robust optimization (DRO) studies decision problems under uncertainty where the probability distribution governing the uncertain problem parameters is itself uncertain. A key component of any DRO model is its ambiguity set, that is, a family of probability distributions consistent with any available structural or statistical ...
Daniel Kuhn, Soroosh Shafiee, Wolfram Wiesemann   +2 more
openaire   +3 more sources

Kernel Distributionally Robust Optimization

, 2020
We propose kernel distributionally robust optimization (Kernel DRO) using insights from the robust optimization theory and functional analysis. Our method uses reproducing kernel Hilbert spaces (RKHS) to construct a wide range of convex ambiguity sets, which can be generalized to sets based on integral probability metrics and finite-order moment bounds.
Zhu, Jia-Jie, Jitkrittum, Wittawat, Diehl, Moritz, Schölkopf, Bernhard   +3 more
openaire   +3 more sources

Distributionally robust optimization for integrated energy distribution network considering vine Copula uncertainty of wind power, photovoltaic and demand side response

Diance yu yibiao
In order to adapt to the uncertainty of new energy, increase new energy consumption and reduce carbon emissions, an optimal scheduling model of integrated energy distribution network system is proposed based on vine Copula considering three uncertainties
YANG Mingjie, HU Yangyu, QIAN Haixia, LIU Fang, WANG Xingkai, XIAO Yuxiang   +5 more
doaj   +1 more source

Distributionally Robust Bayesian Optimization

, 2020
Accepted at AISTATS ...
Kirschner, Johannes, Bogunovic, Ilija, Jegelka, Stefanie, Krause, Andreas   +3 more
openaire   +3 more sources

Semi-supervised Learning based on Distributionally Robust Optimization

, 2019
We propose a novel method for semi-supervised learning (SSL) based on data-driven distributionally robust optimization (DRO) using optimal transport metrics.
Balsubramani A., Blanchet J.H., Blum A., Ghosh S., Grandvalet Y., Luenberger D.G., Namkoong H., Rubner Y., Shafieezadeh‐Abadeh S., Villani C., Volpi R., Xu H., Zhu X.   +12 more
core   +1 more source

Nonlinear distributionally robust optimization

Mathematical Programming
This article focuses on a class of distributionally robust optimization (DRO) problems where, unlike the growing body of the literature, the objective function is potentially nonlinear in the distribution. Existing methods to optimize nonlinear functions in probability space use the Frechet derivatives, which present theoretical and computational ...
Mohammed Rayyan Sheriff, Peyman Mohajerin Esfahani   +1 more
openaire   +3 more sources

Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems [PDF]

, 2020
Learning-based control algorithms require data collection with abundant supervision for training. Safe exploration algorithms ensure the safety of this data collection process even when only partial knowledge is available.
Anandkumar, Anima, Chung, Soon-Jo, Liu, Anqi, Nakka, Yashwanth Kumar, Shi, Guanya, Yue, Yisong   +5 more
core   +1 more source

Distributionally Robust Optimization with Moment Ambiguity Sets

Journal of Scientific Computing, 2022
AbstractThis paper studies distributionally robust optimization (DRO) when the ambiguity set is given by moments for the distributions. The objective and constraints are given by polynomials in decision variables. We reformulate the DRO with equivalent moment conic constraints.
Jiawang Nie, Liu Yang, Suhan Zhong, Guangming Zhou   +3 more
openaire   +3 more sources