Results 41 to 50 of about 9,334 (269)

High-Dimensional Distributionally Robust Mean-Variance Efficient Portfolio Selection

Mathematics, 2023
This paper introduces a novel distributionally robust mean-variance portfolio estimator based on the projection robust Wasserstein (PRW) distance. This approach addresses the issue of increasing conservatism of portfolio allocation strategies due to high-
Zhonghui Zhang, Huarui Jing, Chihwa Kao
doaj   +1 more source

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

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

Distributionally Robust Language Modeling [PDF]

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019
Language models are generally trained on data spanning a wide range of topics (e.g., news, reviews, fiction), but they might be applied to an a priori unknown target distribution (e.g., restaurant reviews). In this paper, we first show that training on text outside the test distribution can degrade test performance when using standard maximum ...
Yonatan Oren, Shiori Sagawa, Tatsunori B. Hashimoto, Percy Liang   +3 more
openaire   +2 more sources

Distributionally robust chance constrained planning model for energy storage plants based on Kullback–Leibler divergence

Energy Reports, 2021
Nowadays, the high penetration of renewable energy, with variable and unpredictable nature, poses major challenges to operation and planning studies of power systems.
Jian Le, Xiaobing Liao, Lina Zhang, Tao Mao   +3 more
doaj   +1 more source

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

Data-driven Distributionally Adjustable Robust Chance-constrained DG Capacity Assessment

Journal of Modern Power Systems and Clean Energy
Moving away from fossil fuels towards renewable sources requires system operators to determine the capacity of distribution systems to safely accommodate green and distributed generation (DG).
Masoume Mahmoodi, Seyyed Mahdi Noori Rahim Abadi, Ahmad Attarha, Paul Scott, Lachlan Blackhall   +4 more
doaj   +1 more source

Distributionally Robust Low-Carbon Scheduling Model for Virtual Power Plants Considering Emerging Distributed Resources and Electricity Carbon Trading [PDF]

Dianli jianshe
[Objective] To improve the low-carbon economic performance of scheduling strategies for virtual power plants， this study proposes a distributionally robust low-carbon scheduling model that incorporates emerging distributed resources and electricity ...
WANG Jiayi, HE Shuaijia
doaj   +1 more source

Research on distributionally robust optimization method considering the flexibility of power grids along CZ railway

Energy Reports, 2023
To avoid the problem of insufficient flexibility of the power grid along the cz railway due to source-load fluctuations, a distributionally robust optimization method considering flexibility is proposed in this paper.
Jiawei Liu, Min Li, Jing Gou, Wenhao Sun, Qiao Zhang, Zhigang Liu   +5 more
doaj   +1 more source

Data-driven Inverse Optimization with Imperfect Information

, 2017
In data-driven inverse optimization an observer aims to learn the preferences of an agent who solves a parametric optimization problem depending on an exogenous signal.
Esfahani, Peyman Mohajerin, Hanasusanto, Grani Adiwena, Kuhn, Daniel, Shafieezadeh-Abadeh, Soroosh   +3 more
core   +1 more source