34 citations · 100 across the 25 of their papers we have counts for
7 papers · 1 filter
Mercer Features for Efficient Combinatorial Bayesian Optimization
Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa
Bayesian optimization (BO) is an efficient framework for solving black-box optimization problems with expensive function evaluations. This paper addresses the BO problem setting fo…
Optimizing Discrete Spaces via Expensive Evaluations: A Learning to Search Framework
Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa +1
We consider the problem of optimizing expensive black-box functions over discrete spaces (e.g., sets, sequences, graphs). The key challenge is to select a sequence of combinatorial…
Multi-Fidelity Multi-Objective Bayesian Optimization: An Output Space Entropy Search Approach
Syrine Belakaria, Aryan Deshwal, Janardhan Rao Doppa
We study the novel problem of blackbox optimization of multiple objectives via multi-fidelity function evaluations that vary in the amount of resources consumed and their accuracy.…
Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations
Syrine Belakaria, Aryan Deshwal, Janardhan Rao Doppa
Many real-world applications involve black-box optimization of multiple objectives using continuous function approximations that trade-off accuracy and resource cost of evaluation.…
Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraints
Syrine Belakaria, Aryan Deshwal, Janardhan Rao Doppa
We consider the problem of constrained multi-objective blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto set of solutions…
Uncertainty aware Search Framework for Multi-Objective Bayesian Optimization with Constraints
Syrine Belakaria, Aryan Deshwal, Janardhan Rao Doppa
We consider the problem of constrained multi-objective (MO) blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto set of solu…