Distributed Distributionally Robust Optimization With Non Convex Objectives Deepai

Distributed Distributionally Robust Optimization With Non-Convex Objectives | DeepAI

Distributed Distributionally Robust Optimization With Non-Convex Objectives | DeepAI To this end, we propose an asynchronous distributed algorithm, named asynchronous single loop alternative gradient projection (aspire) algorithm with the iterative active set method (ease) to tackle the distributed distributionally robust optimization (ddro) problem. Distributionally robust optimization (dro), which aims to find an optimal decision that minimizes the worst case cost over the ambiguity set of probability distribution, has been widely applied in diverse applications, e.g., network behavior analysis, risk management, etc.

Differentially Private Optimization For Smooth Nonconvex ERM | DeepAI

Differentially Private Optimization For Smooth Nonconvex ERM | DeepAI An online method for a class of distributionally robust optimization with non convex objectives. advances in neural information processing systems, 34:10067–10080, 2021. Abstract: distributionally robust optimization (dro), which aims to find an optimal decision that minimizes the worst case cost over the ambiguity set of probability distribution, has been applied in diverse applications, e.g., network behavior analysis, risk management, etc. Abstract—distributionally robust optimization (dro), which aims to find an optimal decision that minimizes the worst case cost over the ambiguity set of probability distribution, has been widely applied in diverse applications, e.g., network behavior analysis, risk management, etc. Abstract: distributionally robust optimization (dro), which aims to find an optimal decision that minimizes the worst case cost over the ambiguity set of probability distribution, has been widely applied in diverse applications, e.g., network behavior analysis, risk management, etc.

Unifying Distributionally Robust Optimization Via Optimal Transport Theory | DeepAI

Unifying Distributionally Robust Optimization Via Optimal Transport Theory | DeepAI Abstract—distributionally robust optimization (dro), which aims to find an optimal decision that minimizes the worst case cost over the ambiguity set of probability distribution, has been widely applied in diverse applications, e.g., network behavior analysis, risk management, etc. Abstract: distributionally robust optimization (dro), which aims to find an optimal decision that minimizes the worst case cost over the ambiguity set of probability distribution, has been widely applied in diverse applications, e.g., network behavior analysis, risk management, etc. In this paper, we propose a practical online method for solving a class of dis tributionally robust optimization (dro) with non convex objectives, which has important applications in machine learning for improving the robustness of neural networks. Proceedings of 36th conference on neural information processing systems (neurips). By carefully exploiting the specific form of the dro objective, we are able to provide non asymptotic convergence guarantees even though the objective function is possibly non convex, non smooth and has unbounded gradient noise. In this paper, we propose a practical online method for solving a class of dis tributionally robust optimization (dro) with non convex objectives, which has important applications in machine learning for improving the robustness of neural networks.

Distributionally Robust Learning With Weakly Convex Losses: Convergence Rates And Finite-Sample ...

Distributionally Robust Learning With Weakly Convex Losses: Convergence Rates And Finite-Sample ... In this paper, we propose a practical online method for solving a class of dis tributionally robust optimization (dro) with non convex objectives, which has important applications in machine learning for improving the robustness of neural networks. Proceedings of 36th conference on neural information processing systems (neurips). By carefully exploiting the specific form of the dro objective, we are able to provide non asymptotic convergence guarantees even though the objective function is possibly non convex, non smooth and has unbounded gradient noise. In this paper, we propose a practical online method for solving a class of dis tributionally robust optimization (dro) with non convex objectives, which has important applications in machine learning for improving the robustness of neural networks.

From Moderate Deviations Theory to Distributionally Robust Optimization: Correlated Data