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End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty
Many decision processes in artificial intelligence and operations research are modeled by parametric optimization problems whose defining parameters are unknown and must be inferred from observable data.
Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimization
The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks.
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