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Found 7 papers, 0 papers with code
Learning-Augmented Scheduling for Solar-Powered Electric Vehicle Charging
This policy employs a dynamic robustness budget, which is adapted in real-time based on the reinforcement learning policy's performance.
Hierarchical Game for Coupled Power System with Energy Sharing and Transportation System
This necessitates new business models in the power sector to hedge against uncertainties while imposing a strong coupling between the connected power and transportation networks.
Equipping Black-Box Policies with Model-Based Advice for Stable Nonlinear Control
Machine-learned black-box policies are ubiquitous for nonlinear control problems.
Robustness and Consistency in Linear Quadratic Control with Untrusted Predictions
Motivated by online learning methods, we design a self-tuning policy that adaptively learns the trust parameter $\lambda$ with a competitive ratio that depends on $\varepsilon$ and the variation of system perturbations and predictions.
TENSILE: A Tensor granularity dynamic GPU memory scheduling method toward multiple dynamic workloads system
In this paper, we demonstrated TENSILE, a method of managing GPU memory in tensor granularity to reduce the GPU memory peak, considering the multiple dynamic workloads.
Disentangling Observed Causal Effects from Latent Confounders using Method of Moments
Thus, we provide a principled approach to tackling the joint problem of causal discovery and latent variable inference.
Learning-Based Predictive Control via Real-Time Aggregate Flexibility
To be used effectively, an aggregator must be able to communicate the available flexibility of the loads they control, as known as the aggregate flexibility to a system operator.
Optimization and Control Systems and Control Systems and Control
