3 citations · 6 across the 2 of their papers we have counts for
7 papers
Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings
Aryan Deshwal, Sebastian Ament, Maximilian Balandat +3
We consider the problem of optimizing expensive black-box functions over high-dimensional combinatorial spaces which arises in many science, engineering, and ML applications. We us…
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…
Optimizing Coverage and Capacity in Cellular Networks using Machine Learning
Ryan M. Dreifuerst, Samuel Daulton, Yuchen Qian +7
Wireless cellular networks have many parameters that are normally tuned upon deployment and re-tuned as the network changes. Many operational parameters affect reference signal rec…
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
Shali Jiang, Daniel R. Jiang, Maximilian Balandat +3
Bayesian optimization is a sequential decision making framework for optimizing expensive-to-evaluate black-box functions. Computing a full lookahead policy amounts to solving a hig…
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization
Samuel Daulton, Maximilian Balandat, Eytan Bakshy
In many real-world scenarios, decision makers seek to efficiently optimize multiple competing objectives in a sample-efficient fashion. Multi-objective Bayesian optimization (BO) i…
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…