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Imitation-regularized Optimal Transport on Networks: Provable Robustness and Application to Logistics Planning
Considering this, there is a pressing need for research to enhance the robustness of network systems.
Resilience Evaluation of Entropy Regularized Logistic Networks with Probabilistic Cost
In this study, we proposed a method for designing a resilient logistics network based on entropy regularization.
Binary Classification from Positive Data with Skewed Confidence
Positive-confidence (Pconf) classification [Ishida et al., 2018] is a promising weakly-supervised learning method which trains a binary classifier only from positive data equipped with confidence.
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