TS-Neyman uses posterior sampling of stratum variances to implement an adaptive Neyman allocation rule that converges almost surely to the oracle proportions and achieves near-oracle efficiency in finite-strata settings.
A review of mixed-integer optimization with ReLU neural networks: Formulations and applications
2 Pith papers cite this work, alongside 77 external citations. Polarity classification is still indexing.
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NEO-Grid trains ReLU networks as power-flow surrogates and applies deep equilibrium models for closed-loop volt-var optimization and control, reporting better voltage regulation than linear and heuristic baselines on the IEEE 33-bus test system.
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TS-Neyman: Posterior Sampling for Adaptive Stratified Estimation
TS-Neyman uses posterior sampling of stratum variances to implement an adaptive Neyman allocation rule that converges almost surely to the oracle proportions and achieves near-oracle efficiency in finite-strata settings.
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NEO-Grid: A Neural Approximation Framework for Optimization and Control in Distribution Grids
NEO-Grid trains ReLU networks as power-flow surrogates and applies deep equilibrium models for closed-loop volt-var optimization and control, reporting better voltage regulation than linear and heuristic baselines on the IEEE 33-bus test system.