Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Svetozarevic, Bratislav"'
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. Inspired by the likelihood ratio idea, we construct a confidence set of t
Externí odkaz:
http://arxiv.org/abs/2402.05367
Autor:
Di Natale, Loris, Zakwan, Muhammad, Svetozarevic, Bratislav, Heer, Philipp, Ferrari-Trecate, Giancarlo, Jones, Colin N.
Machine Learning (ML) and linear System Identification (SI) have been historically developed independently. In this paper, we leverage well-established ML tools - especially the automatic differentiation framework - to introduce SIMBa, a family of di
Externí odkaz:
http://arxiv.org/abs/2311.03197
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 algorithm is proposed that achieves similar regret/violation bounds as tho
Externí odkaz:
http://arxiv.org/abs/2310.00962
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 discomfort of the occupants is below a given threshold. We formulate it as
Externí odkaz:
http://arxiv.org/abs/2310.00758
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-box problems is given, which covers the existing grey-box optimization
Externí odkaz:
http://arxiv.org/abs/2306.05150
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 functions affected by exogenous time-varying contextual disturbances. A p
Externí odkaz:
http://arxiv.org/abs/2304.06104
Autor:
Xu, Wenjie, Jones, Colin N, Svetozarevic, Bratislav, Laughman, Christopher R., Chakrabarty, Ankush
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 improving closed-loop performance by automatically tuning controller gains o
Externí odkaz:
http://arxiv.org/abs/2301.12099
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 cumbersome to identify and scale, and their accuracy might be hindered
Externí odkaz:
http://arxiv.org/abs/2212.12380
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 policies. On the other hand, we postulate that expert knowledge of the
Externí odkaz:
http://arxiv.org/abs/2211.16691
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 unknown system with unmodeled constraints. Existing Gaussian process base
Externí odkaz:
http://arxiv.org/abs/2211.11822