5 citations · 5 across the 1 of their papers we have counts for
3 papers · 1 filter
Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement
Samuel Daulton, Maximilian Balandat, Eytan Bakshy
Optimizing multiple competing black-box objectives is a challenging problem in many fields, including science, engineering, and machine learning. Multi-objective Bayesian optimizat…
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints
Samuel Daulton, Shaun Singh, Vashist Avadhanula +2
Recent advances in contextual bandit optimization and reinforcement learning have garnered interest in applying these methods to real-world sequential decision making problems. Rea…
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
Maximilian Balandat, Brian Karrer, Daniel R. Jiang +4
Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental…