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Found 5 papers, 2 papers with code
Lower Bounds on the Worst-Case Complexity of Efficient Global Optimization
In this paper, we study the worst-case complexity of the efficient global optimization problem and, in contrast to existing kernel-specific results, we derive a unified lower bound for the complexity of efficient global optimization in terms of the metric entropy of a ball in its corresponding reproducing kernel Hilbert space~(RKHS).
Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning
This manuscript offers the perspective of experimentalists on a number of modern data-driven techniques: model predictive control relying on Gaussian processes, adaptive data-driven control based on behavioral theory, and deep reinforcement learning.
Experimental Data-Driven Model Predictive Control of a Hospital HVAC System During Regular Use
Herein we report a multi-zone, heating, ventilation and air-conditioning (HVAC) control case study of an industrial plant responsible for cooling a hospital surgery center.
Robust Uncertainty Bounds in Reproducing Kernel Hilbert Spaces: A Convex Optimization Approach
The problem of establishing out-of-sample bounds for the values of an unkonwn ground-truth function is considered.
Deterministic error bounds for kernel-based learning techniques under bounded noise
We consider the problem of reconstructing a function from a finite set of noise-corrupted samples.
