Preference-Aware Constrained Multi-Objective Bayesian Optimization (Student Abstract)


  • Alaleh Ahmadianshalchi Washington State University
  • Syrine Belakaria Stanford university
  • Janardhan Rao Doppa Washington State University



Multi-objective Optimization, Bayesian Optimization, Constrained Optimization, Probabilistic Models


We consider the problem of constrained multi-objective optimization over black-box objectives, with user-defined preferences, with a largely infeasible input space. Our goal is to approximate the optimal Pareto set from the small fraction of feasible inputs. The main challenges include huge design space, multiple objectives, numerous constraints, and rare feasible inputs identified only through expensive experiments. We present PAC-MOO, a novel preference-aware multi-objective Bayesian optimization algorithm to solve this problem. It leverages surrogate models for objectives and constraints to intelligently select the sequence of inputs for evaluation to achieve the target goal.



How to Cite

Ahmadianshalchi, A., Belakaria, S., & Doppa, J. R. (2024). Preference-Aware Constrained Multi-Objective Bayesian Optimization (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23436-23438.