MIPaaL differentiates through mixed integer programs via cutting planes to enable decision-focused learning for general MIPs, outperforming two-stage prediction-plus-optimization and LP-relaxation baselines on real-world domains.
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Introduces a framework that learns an uncertainty-aware dynamics model and optimizes the policy via automatic differentiation through the model, reporting competitive asymptotic performance with significantly lower sample complexity than baselines on continuous control benchmarks.
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MIPaaL: Mixed Integer Program as a Layer
MIPaaL differentiates through mixed integer programs via cutting planes to enable decision-focused learning for general MIPs, outperforming two-stage prediction-plus-optimization and LP-relaxation baselines on real-world domains.
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Uncertainty-aware Model-based Policy Optimization
Introduces a framework that learns an uncertainty-aware dynamics model and optimizes the policy via automatic differentiation through the model, reporting competitive asymptotic performance with significantly lower sample complexity than baselines on continuous control benchmarks.