The paper surveys EV charging literature through a Planning-Scheduling-Behavior framework and diagnoses a fidelity-tractability trilemma in cross-layer integration.
Smart “Predict, then Optimize
5 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
Introduces decision-aware proximal bridge learning using a weighted loss and regret bound to enhance optimal treatment selection in settings with hidden confounding.
IGT-OMD reduces gradient transport error from quadratic to linear in delay length for delayed bilevel optimization and achieves sublinear regret with adaptive steps.
Introduces a decision-aware neural ODE model that integrates outage dynamics prediction with global optimization of resilience interventions for power grids.
A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.
citing papers explorer
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Planning, Scheduling, and Behavior in EV Charging Systems: A Critical Survey and Trilemma Framework
The paper surveys EV charging literature through a Planning-Scheduling-Behavior framework and diagnoses a fidelity-tractability trilemma in cross-layer integration.
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Decision-Aware Proximal Bridge Learning for Optimal Treatment Selection
Introduces decision-aware proximal bridge learning using a weighted loss and regret bound to enhance optimal treatment selection in settings with hidden confounding.
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IGT-OMD: Implicit Gradient Transport for Decision-Focused Learning under Delayed Feedback
IGT-OMD reduces gradient transport error from quadratic to linear in delay length for delayed bilevel optimization and achieves sublinear regret with adaptive steps.
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Global-Decision-Focused Neural ODEs for Proactive Grid Resilience Management
Introduces a decision-aware neural ODE model that integrates outage dynamics prediction with global optimization of resilience interventions for power grids.
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Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers
A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.