5 papers
Improving Bayesian Optimization via Training-Aware Conditional Diffusion Models
Yilin Zheng, Haowei Wang, Szu Hui Ng +1
Bayesian optimization (BO) is a widely used approach for black-box optimization that uses a Gaussian process (GP) as a surrogate and guides sequential evaluations via an acquisitio…
Pragmatic Curiosity: A Unified Framework for Hybrid Learning and Optimization via Active Inference
Yingke Li, Anjali Parashar, Enlu Zhou +1
Many engineering and scientific workflows rely on expensive black-box evaluations, requiring sequential decisions that must both improve task performance and reduce uncertainty. Ba…
Stochastic Optimal Control with Side Information and Bayesian Learning
Johannes Milz, Alexander Shapiro, Enlu Zhou
We study infinite-horizon stochastic optimal control problems with observable side information: a Markov chain that modulates an unknown context-conditional randomness distribution…
Curiosity is Knowledge: Self-Consistent Learning and No-Regret Optimization with Active Inference
Yingke Li, Anjali Parashar, Enlu Zhou +1
Active inference (AIF) unifies exploration and exploitation by minimizing the Expected Free Energy (EFE), balancing epistemic value (information gain) and pragmatic value (task per…
Bayesian Risk-averse Model Predictive Control with Consistency and Stability Guarantees
Yingke Li, Yifan Lin, Enlu Zhou +1
Model Predictive Control (MPC) is a powerful framework for constrained control, but its performance and safety can be severely degraded when the prediction model is learned online…