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
10 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 10representative 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.
InvEvolve evolves inventory policies using LLMs with RL and provides statistical safety guarantees, outperforming classical and DL methods on synthetic and real data.
A gradient-based sample generation framework is proposed to identify macroeconomic conditions inducing specific behaviors in portfolio optimization pipelines.
Benchmark of 15 time-series architectures on equity portfolios finds no model dominates, with TransEnc-8 at 0.352 rank-1 acceptability and all promoted models showing negative net Sharpe at 20 bps costs under constraints.
A multi-objective probabilistic forecast combination framework is introduced that generates Pareto-optimal combinations balancing forecast accuracy and inventory decision performance, outperforming single-objective methods on retail and spare parts data.
Introduces a decision-aware neural ODE model that integrates outage dynamics prediction with global optimization of resilience interventions for power grids.
A tutorial reviewing why traditional prediction models often fail to improve decision quality in stochastic optimization and summarizing key properties and tools of decision-focused learning.
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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InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees
InvEvolve evolves inventory policies using LLMs with RL and provides statistical safety guarantees, outperforming classical and DL methods on synthetic and real data.
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Generating Input Distributions for Explaining Portfolio Optimization Pipelines
A gradient-based sample generation framework is proposed to identify macroeconomic conditions inducing specific behaviors in portfolio optimization pipelines.
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Benchmarking Deep Time Series Models for Equity Portfolios
Benchmark of 15 time-series architectures on equity portfolios finds no model dominates, with TransEnc-8 at 0.352 rank-1 acceptability and all promoted models showing negative net Sharpe at 20 bps costs under constraints.
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Multi-objective probabilistic forecast combination for inventory demand
A multi-objective probabilistic forecast combination framework is introduced that generates Pareto-optimal combinations balancing forecast accuracy and inventory decision performance, outperforming single-objective methods on retail and spare parts data.
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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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Decision-Focused Learning: When and Why Traditional Prediction Models Fail
A tutorial reviewing why traditional prediction models often fail to improve decision quality in stochastic optimization and summarizing key properties and tools of decision-focused learning.
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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.