Results 111 to 120 of about 8,860,352 (207)

A Differential Planning Strategy for Distribution Network Resilience Enhancement Considering Decision Dependence Uncertainty

Energies
To reduce the impact of extreme natural disasters on urban distribution networks and improve the interpretability of planning decisions, this paper proposes a distributionally robust planning strategy for distribution networks that considers decision ...
Xuming Chen, Le Liu, Xiaoning Kang
doaj   +1 more source

Learning Optimal Distributionally Robust Individualized Treatment Rules. [PDF]

J Am Stat Assoc, 2021
Mo W, Qi Z, Liu Y.
europepmc   +1 more source

Rejoinder: Learning Optimal Distributionally Robust Individualized Treatment Rules. [PDF]

J Am Stat Assoc, 2021
Mo W, Qi Z, Liu Y.
europepmc   +1 more source

Distributionally Robust Federated Learning With Client Drift Minimization

IEEE Transactions on Machine Learning in Communications and Networking
Federated learning (FL) faces critical challenges, particularly in heterogeneous environments where non-independent and identically distributed (non-IID) data across clients can lead to unfair and inefficient model performance. We introduce DRDM, a novel
Mounssif Krouka, Chaouki Ben Issaid, Mehdi Bennis   +2 more
doaj   +1 more source

A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line. [PDF]

Engineering (Beijing), 2022
Lu Y, Yang L, Yang K, Gao Z, Zhou H, Meng F, Qi J.   +6 more
europepmc   +1 more source

Carbon-aware mobile energy storage system scheduling in active power distribution systems under PV and traffic uncertainties

Energy Reports
Carbon emission flow (CEF) is a promising approach for assessing both generation-and consumption-side carbon footprints in the power system sector. In this study, we propose a carbon-aware mobile energy storage system (MESS) scheduling framework that ...
Panggah Prabawa, Dae-Hyun Choi
doaj   +1 more source

Distributionally Robust Kalman Filter

In this work, we propose a noise-centric formulation of the distributionally robust Kalman filter (DRKF) for discrete-time linear stochastic systems with uncertain noise statistics. By placing Wasserstein ambiguity sets directly on the process and measurement noise distributions, the proposed DRKF preserves the analytical structure of the classical ...
Jang, Minhyuk, Hakobyan, Astghik, Yang, Insoon   +2 more
openaire   +2 more sources

Distributionally Robust Treatment Effect

Using only retrospective data, we propose an estimator for predicting the treatment effect for the same treatment/policy to be implemented in another location or time period, which requires no input from the target population. More specifically, we minimize the worst-case mean square error for the prediction of treatment effect within a class of ...
Xu, Ruonan, Yang, Xiye
openaire   +2 more sources

Distributionally Robust Causal Abstractions

Causal Abstraction (CA) theory provides a principled framework for relating causal models that describe the same system at different levels of granularity while ensuring interventional consistency between them. Recently, several approaches for learning CAs have been proposed, but all assume fixed and well-specified exogenous distributions, making them ...
Felekis, Yorgos, Damoulas, Theodoros, Giampouras, Paris   +2 more
openaire   +2 more sources

Distributionally Robust Safe Screening

In this study, we propose a method Distributionally Robust Safe Screening (DRSS), for identifying unnecessary samples and features within a DR covariate shift setting. This method effectively combines DR learning, a paradigm aimed at enhancing model robustness against variations in data distribution, with safe screening (SS), a sparse optimization ...
Hanada, Hiroyuki, Akahane, Satoshi, Aoyama, Tatsuya, Tanaka, Tomonari, Okura, Yoshito, Inatsu, Yu, Hashimoto, Noriaki, Murayama, Taro, Hanju, Lee, Kojima, Shinya, Takeuchi, Ichiro   +10 more
openaire   +2 more sources