Results 21 to 30 of about 9,334 (269)
A Distributionally Robust Boosting Algorithm [PDF]
2019 Winter Simulation Conference (WSC), 2019 Distributionally Robust Optimization (DRO) has been shown to provide a flexible framework for decision making under uncertainty and statistical estimation. For example, recent works in DRO have shown that popular statistical estimators can be interpreted as the solutions of suitable formulated data-driven DRO problems.
Jose H. Blanchet +3 more
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Distributionally Robust Submodular Maximization
CoRR, 2018 Submodular functions have applications throughout machine learning, but in many settings, we do not have direct access to the underlying function $f$. We focus on stochastic functions that are given as an expectation of functions over a distribution $P$. In practice, we often have only a limited set of samples $f_i$ from $P$.
Staib, Matthew +2 more
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Distributionally Robust Federated Averaging
CoRR, 2021 In this paper, we study communication efficient distributed algorithms for distributionally robust federated learning via periodic averaging with adaptive sampling. In contrast to standard empirical risk minimization, due to the minimax structure of the underlying optimization problem, a key difficulty arises from the fact that the global parameter ...
Yuyang Deng +2 more
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IET Renewable Power Generation, 2023 Integrated generation, transmission, and storage expansion planning (IGT&SP) is the cornerstone to realize low‐carbon transition considering security constraints in the long run.
Lu Qiu, Yangqing Dan, Xukun Li, Ye Cao
doaj +1 more source
Data-Driven Chance Constrained Optimization under Wasserstein Ambiguity Sets [PDF]
, 2018 We present a data-driven approach for distributionally robust chance constrained optimization problems (DRCCPs). We consider the case where the decision maker has access to a finite number of samples or realizations of the uncertainty.
Cherukuri, Ashish +2 more
core +2 more sources
The Distributionally Robust Optimization Reformulation for Stochastic Complementarity Problems
Abstract and Applied Analysis, 2014 We investigate the stochastic linear complementarity problem affinely affected by the uncertain parameters. Assuming that we have only limited information about the uncertain parameters, such as the first two moments or the first two moments as well as ...
Liyan Xu, Bo Yu, Wei Liu
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Energy Reports, 2021 The microgrid (MG) is an effective way to alleviate the impact of the large-scale penetration of distributed generations. Due to the seasonal characteristics of rural areas, the load curve of the rural MG is different from the urban MG.
Changming Chen +9 more
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Distributionally Robust Data Join
CoRR, 2022 Suppose we are given two datasets: a labeled dataset and unlabeled dataset which also has additional auxiliary features not present in the first dataset. What is the most principled way to use these datasets together to construct a predictor? The answer should depend upon whether these datasets are generated by the same or different distributions over ...
Awasthi, Pranjal +2 more
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Distributionally Robust Mechanism Design [PDF]
Management Science, 2020 We study a mechanism design problem in which an indivisible good is auctioned to multiple bidders for each of whom it has a private value that is unknown to the seller and the other bidders. The agents perceive the ensemble of all bidder values as a random vector governed by an ambiguous probability distribution, which belongs to a commonly known ...
Çağıl Koçyiğit +3 more
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Probabilistic Optimization Techniques in Smart Power System
Energies, 2022 Uncertainties are the most significant challenges in the smart power system, necessitating the use of precise techniques to deal with them properly. Such problems could be effectively solved using a probabilistic optimization strategy.
Muhammad Riaz +4 more
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