TILT adds a target-data penalty on an auxiliary predictor component to induce effective importance weighting for unsupervised domain adaptation under covariate shift.
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SiMPL generates feasible iterates for multi-material topology optimization by using tailored Bregman divergences to enforce point-wise polytopal design constraints, with global constraints handled via a small dual problem.
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TILT: Target-induced loss tilting under covariate shift
TILT adds a target-data penalty on an auxiliary predictor component to induce effective importance weighting for unsupervised domain adaptation under covariate shift.
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The SiMPL Method for Multi-Material Topology Optimization
SiMPL generates feasible iterates for multi-material topology optimization by using tailored Bregman divergences to enforce point-wise polytopal design constraints, with global constraints handled via a small dual problem.