Safe Bayesian Optimization for Uncertain Correlation Matrices in Linear Models of Co-Regionalization
This paper extends safety guarantees for multi-task Bayesian optimization with uncertain co-regionalization matrices from intrinsic co-regionalization models to linear models of co-regionalization. The latter allows for more flexible modeling of the inter-task correlations by composing multiple features. We derive uniform error bounds for vector-valued functions sampled from a Gaussian process with a linear model of co-regionalization kernel. Furthermore, we show the potential performance gains of linear models of co-regionalization in a numerical comparison on a safe multi-task Bayesian optimization benchmark.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Meta-Learning Priors for Safe Bayesian Optimization
In robotics, optimizing controller parameters under safety constraints is an important challenge. Safe Bayesian optimization (BO) quantifies uncertainty in the objective and constraints to safely guide exploration in suc…
Bayesian OptimizationMeta-LearningUncertainty QuantificationSafe Bayesian Optimization with Counterfactual Policies
In many decision-making settings, new interventions are acceptable only if they do not reduce outcomes below some established threshold. For example, in clinical medicine, new treatments are often acceptable only if they…
Transductive Active Learning with Application to Safe Bayesian Optimization
Safe Bayesian optimization (Safe BO) is the task of learning an optimal policy within an unknown environment, while ensuring that safety constraints are not violated. We analyze Safe BO under the lens of a generalization…
Active LearningBayesian OptimizationPredictionReinforcement Learning (RL)+3Safety in safe Bayesian optimization and its ramifications for control
A recurring and important task in control engineering is parameter tuning under constraints, which conceptually amounts to optimization of a blackbox function accessible only through noisy evaluations. For example, in co…
Bayesian OptimizationConstrained Policy Optimization via Bayesian World Models
Improving sample-efficiency and safety are crucial challenges when deploying reinforcement learning in high-stakes real world applications. We propose LAMBDA, a novel model-based approach for policy optimization in safet…
reinforcement-learningReinforcement Learning (RL)