Robust Solutions of Optimization Problems Affected by Uncertain Probabilities

By Aharon Ben-Tal, Dick den Hertog, Anja De Waegenaere, Bertrand Melenberg, Gijs Rennen. 2013

1,035 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability, Mathematical Optimization

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Citations in the corpus

Fairness Without Demographics in Repeated Loss Minimization (2018, methodology): “While there are many possible robustness balls B which could provide upper bounds on group risk, we opt to use the chi-squared ball since it is straightforward to optimize (Ben-Tal et al., 2013; Namkoong & Duchi, 2016; 2017) and we found it empirically outperformed other f -divergence balls.”
Certifying Some Distributional Robustness with Principled Adversarial Training (2017, background): “, 2007; Delage & Ye, 2010; Goh & Sim, 2010), as well as non-parametric distances for probability measures such as f -divergences (Ben-Tal et al., 2013; Bertsimas et al., 2013; Lam & Zhou, 2015; Miyato et al., 2015; Duchi et al., 2016; Namkoong & Duchi, 2016), and Wasserstein distances (Esfahani & Ku”
Conditional variance penalties and domain shift robustness (2017, background): “, 2017; Besserve et al., 2018; Kocaoglu et al., 2018). Kilbertus et al. (2017) exploit causal reasoning to characterize fairness considerations in machine learning.”
Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization (2019, background): “In the convex and batch case, there is a rich literature on distributionally robust optimization which treats the problem as a standard convex conic program (Ben-Tal et al., 2013; Duchi et al., 2016; Bertsimas et al., 2018; Lam & Zhou, 2015).”
Distributionally Robust Neural Networks (2020, background): “This is an instance of distributionally robust optimization (DRO), which studies worst-case performance over potential test distributions (Ben-Tal et al., 2013; Duchi et al., 2016).”
Robustness to Spurious Correlations via Human Annotations (2020, methodology): “, 2018), and robust optimization (Ben-Tal et al., 2013; Duchi & Namkoong, 2018; Bertsimas et al., 2018; Lam & Zhou, 2015), few prior works seek to elicit information on the possible shifts in unmeasured variables.”
Kullback-Leibler divergence constrained distributionally robust optimization (2012, methodology): “Ben-Tal et al. (2012) considered the robust optimization problems where ambiguities are modeled using various φ-divergence measures.”
Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach (2016, methodology): “[8] for distributions P with finite support.”
An Investigation of Why Overparameterization Exacerbates Spurious Correlations (2020, background): “To mitigate this problem, common approaches reduce the worst-group training loss, e.g., through distributionally robust optimization (DRO) or simply upweighting the minority groups.”
MEMO: Test Time Robustness via Adaptation and Augmentation (2021, background): “, 2013; Gulrajani & Lopez-Paz, 2021), and distributionally robust optimization (Ben-Tal et al., 2013; Hu et al., 2018; Sagawa et al., 2020), to name just a few.”
Robust empirical optimization is almost the same as mean-variance optimization (2018, neutral): “While it has some desirable asymptotic properties, outof-sample performance can be poor when the sample size is not sufficiently large or the statistical assumptions that form the basis of empirical optimization are not valid, and robust (worst-case) empirical optimization has emerged as one approac”
Robust Optimization for Fairness with Noisy Protected Groups (2020, methodology): “The first approach we propose (Section 5) is based on distributionally robust optimization (DRO) [19, 8].”
Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences (2016, background): “Instead, we view the robust ERM problem (1) as a game between the x (minimizing) player and p (maximizing) player.”
Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift (2020, methodology): “DRO methods optimize machine learning systems to be robust to adversarial data distributions, thus optimizing for worst case performance against distribution shift [21, 5, 42, 16, 47, 15, 7].”