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Showing 1 to 4 of 4 for “"fast approximations"”.
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Fast approximations for combinatorial optimization via multiplicative weight updates
"We develop fast approximations for several LP relaxations that arise in discrete and combinatorial optimization. New results include improved running times for explicit mixed packing and covering problems, nearly linear time approximations for tree packings, nearly linear time approximations for …
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Probabilistic and Statistical Learning Models for Error Modeling and Uncertainty Quantification
… herein are inherently stochastic, and numerical approximations suffer from stability and accuracy issues. The second class of models are partial differential equations, which capture the laws of mathematical physics; however, they only approximate a more complex reality, and have uncertainties …
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Machine learning for electronic structure
… of applications, largely due to the fact that ML approximations have a much lower computational cost in comparison to electronic structure calculations, while maintaining comparable levels of accuracy. However, most of these models are designed to predict a predetermined set of quantum chemical …
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Aligning Machine Learning and Robust Decision-Making
… machine learning framework to learn fast approximations to general convex problems. We further apply this within an end-to-end learning framework which trains ML models with an optimization-based loss function to minimize the decision cost directly. This meta-optimization approach …