DR-MOO adds distributional robustness to multi-objective optimization and gives single-loop MGDA algorithms reaching epsilon-Pareto-stationary points in O(epsilon^{-4}) samples for nonconvex problems.
Advances in neural information processing systems , volume=
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A barrier-enforced multi-objective optimization framework for neural networks generates sharp non-crossing prediction intervals that meet exact target coverage in probabilistic forecasting.
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Distributionally Robust Multi-Objective Optimization
DR-MOO adds distributional robustness to multi-objective optimization and gives single-loop MGDA algorithms reaching epsilon-Pareto-stationary points in O(epsilon^{-4}) samples for nonconvex problems.
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Barrier-enforced multi-objective optimization for direct point and sharp interval forecasting
A barrier-enforced multi-objective optimization framework for neural networks generates sharp non-crossing prediction intervals that meet exact target coverage in probabilistic forecasting.