11 citations · 17 across the 9 of their papers we have counts for
10 papers
Robust Error Bounds for Vector-Valued Kernel Ridge Regression
Wenbin Wang, Colin N. Jones
While kernel-based learning methods for scalar-valued functions have been extensively studied, comparatively little work considers nonlinear vector-valued operators. Such operators…
Bayesian Optimization with Structured Measurements: A Vector-Valued RKHS Framework
Wenbin Wang, Colin N. Jones
Bayesian optimization (BO) is an efficient framework for optimizing expensive black-box functions. However, it is typically formulated as learning an end-to-end mapping from inputs…
Learning efficient representations of complex constraints for scalable optimization
Yilin Wen, Yi Guo, Bo Zhao +4
Complex constraints often make real-world optimization computationally prohibitive at the scale and speed required for operational decision-making. Here we introduce PolyFormer, a…
Principled Bayesian Optimisation in Collaboration with Human Experts
Wenjie Xu, Masaki Adachi, Colin N. Jones +1
Bayesian optimisation for real-world problems is often performed interactively with human experts, and integrating their domain knowledge is key to accelerate the optimisation proc…
Principled Preferential Bayesian Optimization
Wenjie Xu, Wenbin Wang, Yuning Jiang +2
We study the problem of preferential Bayesian optimization (BO), where we aim to optimize a black-box function with only preference feedback over a pair of candidate solutions. Ins…
SIMBa: System Identification Methods leveraging Backpropagation
Loris Di Natale, Muhammad Zakwan, Philipp Heer +2
This manuscript details and extends the SIMBa toolbox (System Identification Methods leveraging Backpropagation) presented in previous work, which uses well-established Machine Lea…
Stable Linear Subspace Identification: A Machine Learning Approach
Loris Di Natale, Muhammad Zakwan, Bratislav Svetozarevic +3
Machine Learning (ML) and linear System Identification (SI) have been historically developed independently. In this paper, we leverage well-established ML tools - especially the au…
Multi-Agent Bayesian Optimization with Coupled Black-Box and Affine Constraints
Wenjie Xu, Yuning Jiang, Bratislav Svetozarevic +1
This paper studies the problem of distributed multi-agent Bayesian optimization with both coupled black-box constraints and known affine constraints. A primal-dual distributed algo…
Data-driven adaptive building thermal controller tuning with constraints: A primal-dual contextual Bayesian optimization approach
Wenjie Xu, Bratislav Svetozarevic, Loris Di Natale +2
We study the problem of tuning the parameters of a room temperature controller to minimize its energy consumption, subject to the constraint that the daily cumulative thermal disco…
Constrained Efficient Global Optimization of Expensive Black-box Functions
Wenjie Xu, Yuning Jiang, Bratislav Svetozarevic +1
We study the problem of constrained efficient global optimization, where both the objective and constraints are expensive black-box functions that can be learned with Gaussian proc…