June 6, 2024, 4:42 a.m. | Alan A. Lahoud, Erik Schaffernicht, Johannes A. Stork

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.03082v1 Announce Type: new
Abstract: Mathematical solvers use parametrized Optimization Problems (OPs) as inputs to yield optimal decisions. In many real-world settings, some of these parameters are unknown or uncertain. Recent research focuses on predicting the value of these unknown parameters using available contextual features, aiming to decrease decision regret by adopting end-to-end learning approaches. However, these approaches disregard prediction uncertainty and therefore make the mathematical solver susceptible to provide erroneous decisions in case of low-confidence predictions. We propose a …

abstract arxiv bayesian cs.lg decision decisions features inputs networks neural networks ops optimization parameters research solutions stochastic type uncertain value world

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