Results 11 to 20 of about 569 (214)
Distributionally robust profit opportunities [PDF]
Operations Research Letters, 2021 arXiv admin note: text overlap with arXiv:2004 ...
Derek Singh, Shuzhong Zhang
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Distributionally Robust Learning [PDF]
Foundations and Trends® in Optimization, 2020 This monograph develops a comprehensive statistical learning framework that is robust to (distributional) perturbations in the data using Distributionally Robust Optimization (DRO) under the Wasserstein metric. Beginning with fundamental properties of the Wasserstein metric and the DRO formulation, we explore duality to arrive at tractable formulations
Ruidi Chen, Ioannis Ch. Paschalidis
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Distributionally Robust Domain Adaptation
2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP), 2023 Domain Adaptation (DA) has recently received significant attention due to its potential to adapt a learning model across source and target domains with mismatched distributions. Since DA methods rely exclusively on the given source and target domain samples, they generally yield models that are vulnerable to noise and unable to adapt to unseen samples ...
Akram S. Awad, George K. Atia
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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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Distributionally Robust Transfer Learning
CoRR, 2023 Many existing transfer learning methods rely on leveraging information from source data that closely resembles the target data. However, this approach often overlooks valuable knowledge that may be present in different yet potentially related auxiliary samples.
Xin Xiong 0006 +2 more
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Distributionally Robust Selection of the Best [PDF]
Management Science, 2020 Specifying a proper input distribution is often a challenging task in simulation modeling. In practice, there may be multiple plausible distributions that can fit the input data reasonably well, especially when the data volume is not large. In this paper, we consider the problem of selecting the best from a finite set of simulated alternatives, in the
Weiwei Fan +2 more
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Distributionally Robust and Generalizable Inference
Statistical Science, 2023 We discuss recently developed methods that quantify the stability and generalizability of statistical findings under distributional changes. In many practical problems, the data is not drawn i.i.d. from the target population. For example, unobserved sampling bias, batch effects, or unknown associations might inflate the variance compared to i.i.d ...
Rothenhäusler, Dominik +1 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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