54 citations · 67 across the 16 of their papers we have counts for
16 papers
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…
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…
Bayesian Optimization of Expensive Nested Grey-Box Functions
Wenjie Xu, Yuning Jiang, Bratislav Svetozarevic +1
We consider the problem of optimizing a grey-box objective function, i.e., nested function composed of both black-box and white-box functions. A general formulation for such grey-b…
Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraints
Wenjie Xu, Yuning Jiang, Bratislav Svetozarevic +1
This paper studies the problem of online performance optimization of constrained closed-loop control systems, where both the objective and the constraints are unknown black-box fun…
Violation-Aware Contextual Bayesian Optimization for Controller Performance Optimization with Unmodeled Constraints
Wenjie Xu, Colin N Jones, Bratislav Svetozarevic +2
We study the problem of performance optimization of closed-loop control systems with unmodeled dynamics. Bayesian optimization (BO) has been demonstrated to be effective for improv…
CONFIG: Constrained Efficient Global Optimization for Closed-Loop Control System Optimization with Unmodeled Constraints
Wenjie Xu, Yuning Jiang, Bratislav Svetozarevic +1
In this paper, the CONFIG algorithm, a simple and provably efficient constrained global optimization algorithm, is applied to optimize the closed-loop control performance of an unk…
Towards Scalable Physically Consistent Neural Networks: an Application to Data-driven Multi-zone Thermal Building Models
Loris Di Natale, Bratislav Svetozarevic, Philipp Heer +1
With more and more data being collected, data-driven modeling methods have been gaining in popularity in recent years. While physically sound, classical gray-box models are often c…
Physically Consistent Neural ODEs for Learning Multi-Physics Systems
Muhammad Zakwan, Loris Di Natale, Bratislav Svetozarevic +3
Despite the immense success of neural networks in modeling system dynamics from data, they often remain physics-agnostic black boxes. In the particular case of physical systems, th…
Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules
Loris Di Natale, Bratislav Svetozarevic, Philipp Heer +1
Model-free Reinforcement Learning (RL) generally suffers from poor sample complexity, mostly due to the need to exhaustively explore the state-action space to find well-performing…
Near-optimal Deep Reinforcement Learning Policies from Data for Zone Temperature Control
Loris Di Natale, Bratislav Svetozarevic, Philipp Heer +1
Replacing poorly performing existing controllers with smarter solutions will decrease the energy intensity of the building sector. Recently, controllers based on Deep Reinforcement…
Physically Consistent Neural Networks for building thermal modeling: theory and analysis
Loris Di Natale, Bratislav Svetozarevic, Philipp Heer +1
Due to their high energy intensity, buildings play a major role in the current worldwide energy transition. Building models are ubiquitous since they are needed at each stage of th…
VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints
Wenjie Xu, Colin N Jones, Bratislav Svetozarevic +2
We study the problem of performance optimization of closed-loop control systems with unmodeled dynamics. Bayesian optimization (BO) has been demonstrated effective for improving cl…
Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: simulations and experiments
B. Svetozarevic, C. Baumann, S. Muntwiler +3
This work presents a fully data-driven, black-box pipeline to obtain an optimal control policy for a multi-loop building control problem based on historical building and weather da…
Quick-cast: A method for fast and precise scalable production of fluid-driven elastomeric soft actuators
Bratislav Svetozarevic, Moritz Begle, Stefan Caranovic +2
Fluid-driven elastomeric actuators (FEAs) are among the most popular actuators in the emerging field of soft robotics. Intrinsically compliant, with continuum of motion, large stro…