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Found 5 papers, 0 papers with code
Meta-learning of data-driven controllers with automatic model reference tuning: theory and experimental case study
Data-driven control offers a viable option for control scenarios where constructing a system model is expensive or time-consuming.
Hawkes-based cryptocurrency forecasting via Limit Order Book data
Accurately forecasting the direction of financial returns poses a formidable challenge, given the inherent unpredictability of financial time series.
In-context learning of state estimators
State estimation has a pivotal role in several applications, including but not limited to advanced control design.
Meta-learning for model-reference data-driven control
Assuming that this supplementary information is available, we propose a novel, direct design approach that leverages the data from similar plants, the knowledge of controllers calibrated on them, and the corresponding closed-loop performance to enhance model-reference control design.
META-SMGO-$Δ$: similarity as a prior in black-box optimization
When solving global optimization problems in practice, one often ends up repeatedly solving problems that are similar to each others.
