5 papers
BEACON: A Bayesian Optimization Inspired Strategy for Efficient Novelty Search
Wei-Ting Tang, Ankush Chakrabarty, Joel A. Paulson
Novelty search (NS) aims to uncover diverse system behaviors through simulation or experiment without requiring a pre-specified scalar objective. This capability is especially rele…
Robust Regularized Policy Iteration under Transition Uncertainty
Hongqiang Lin, Zhenghui Fu, Weihao Tang +4
Offline reinforcement learning (RL) enables data-efficient and safe policy learning without online exploration, but its performance often degrades under distribution shift. The lea…
NeST-BO: Fast Local Bayesian Optimization via Newton-Step Targeting of Gradient and Hessian Information
Wei-Ting Tang, Akshay Kudva, Joel A. Paulson
Bayesian optimization (BO) is effective for expensive black-box problems but remains challenging in high dimensions. We propose NeST-BO, a curvature-aware local BO method that targ…
Multi-Objective Bayesian Optimization for Networked Black-Box Systems: A Path to Greener Profits and Smarter Designs
Akshay Kudva, Wei-Ting Tang, Joel A. Paulson
Designing modern industrial systems requires balancing several competing objectives, such as profitability, resilience, and sustainability, while accounting for complex interaction…
CAGES: Cost-Aware Gradient Entropy Search for Efficient Local Multi-Fidelity Bayesian Optimization
Wei-Ting Tang, Joel A. Paulson
Bayesian optimization (BO) is a popular approach for optimizing expensive-to-evaluate black-box objective functions. An important challenge in BO is its application to high-dimensi…