1 citations · 1 across the 4 of their papers we have counts for
4 papers
Beyond Objective-Based Improvement: Stationarity-Aware Expected Improvement for Bayesian Optimization
Joshua Hang Sai Ip, Georgios Makrygiorgos, Ali Mesbah
Bayesian Optimization (BO) is a principled framework for optimizing expensive black-box functions, with Expected Improvement (EI) among its most widely used acquisition functions.…
Towards Scalable Bayesian Optimization via Gradient-Informed Bayesian Neural Networks
Georgios Makrygiorgos, Joshua Hang Sai Ip, Ali Mesbah
Bayesian optimization (BO) is a widely used method for data-driven optimization that generally relies on zeroth-order data of objective function to construct probabilistic surrogat…
User Preference Meets Pareto-Optimality in Multi-Objective Bayesian Optimization
Joshua Hang Sai Ip, Ankush Chakrabarty, Ali Mesbah +1
Incorporating user preferences into multi-objective Bayesian optimization (MOBO) allows for personalization of the optimization procedure. Preferences are often abstracted in the f…
Lyapunov Neural ODE State-Feedback Control Policies
Joshua Hang Sai Ip, Georgios Makrygiorgos, Ali Mesbah
Deep neural networks are increasingly used as an effective parameterization of control policies in various learning-based control paradigms. For continuous-time optimal control pro…