DR-Gym is a new Gymnasium-compatible simulator for training utility demand-response policies with regime-switching wholesale prices and physics-based building demand.
Tyrrell Rockafellar and Stanislav Uryasev
9 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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PUICL is a transformer pretrained on synthetic PU data from structural causal models that solves positive-unlabeled classification via in-context learning without gradient updates or fitting.
Proposes APUB optimization framework for stochastic programming, proves asymptotic correctness and consistency of the new bound, and develops bootstrap and L-shaped solvers for two-stage linear problems with empirical tests on a product mix example.
A Bayesian framework learns uncertainties from data to generate robust multi-topology express network designs that reduce tail delivery risks at modest extra cost in simulations.
A stochastic optimization framework with distributionally robust risk constraints is proposed for dynamic NBA franchise management.
An adaptive digital twin uses online Bayesian updates on transition probabilities in dynamic Bayesian networks, combined with reinforcement learning on parametric MDPs, to enable personalized predictive decision-making for structural health monitoring.
Football fever in spectators follows a V-shaped time course captured as a latent process from heart rate and stress data via time-dependent structural equation modeling.
The paper defines skew engineering as a path-dependent discipline that reduces downside participation more than upside to improve recovery efficiency and compounding under adverse market regimes.
citing papers explorer
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Towards Affordable Energy: A Gymnasium Environment for Electric Utility Demand-Response Programs
DR-Gym is a new Gymnasium-compatible simulator for training utility demand-response policies with regime-switching wholesale prices and physics-based building demand.
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In-Context Positive-Unlabeled Learning
PUICL is a transformer pretrained on synthetic PU data from structural causal models that solves positive-unlabeled classification via in-context learning without gradient updates or fitting.
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Minimizing Upper Confidence Bounds: A Data-Driven Framework for Stochastic Programming
Proposes APUB optimization framework for stochastic programming, proves asymptotic correctness and consistency of the new bound, and develops bootstrap and L-shaped solvers for two-stage linear problems with empirical tests on a product mix example.
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Bayesian Multi-Topology Express Transportation Network Design under Posterior Predictive Demand, Sorting-Efficiency and Delivery-Time Uncertainty
A Bayesian framework learns uncertainties from data to generate robust multi-topology express network designs that reduce tail delivery risks at modest extra cost in simulations.
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A Rolling-Horizon Stochastic Optimization Framework for NBA Franchise Management with Distributionally Robust Risk Constraints
A stochastic optimization framework with distributionally robust risk constraints is proposed for dynamic NBA franchise management.
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Adaptive digital twins for predictive decision-making: Online Bayesian learning of transition dynamics
An adaptive digital twin uses online Bayesian updates on transition probabilities in dynamic Bayesian networks, combined with reinforcement learning on parametric MDPs, to enable personalized predictive decision-making for structural health monitoring.
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Time-dependent structural equation modeling of fans' football fever using activity tracking data during the 2025 DFB Cup final
Football fever in spectators follows a V-shaped time course captured as a latent process from heart rate and stress data via time-dependent structural equation modeling.
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The Engineering of Skew: A Path-Dependent Framework for Asymmetric Volatility Management
The paper defines skew engineering as a path-dependent discipline that reduces downside participation more than upside to improve recovery efficiency and compounding under adverse market regimes.
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