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Found 10 papers, 6 papers with code
Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces
Impactful applications such as materials discovery, hardware design, neural architecture search, or portfolio optimization require optimizing high-dimensional black-box functions with mixed and combinatorial input spaces.
Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces
Recent advances have extended the scope of Bayesian optimization (BO) to expensive-to-evaluate black-box functions with dozens of dimensions, aspiring to unlock impactful applications, for example, in the life sciences, neural architecture search, and robotics.
Scalable Constrained Bayesian Optimization
The global optimization of a high-dimensional black-box function under black-box constraints is a pervasive task in machine learning, control, and engineering.
Bayesian Optimization Allowing for Common Random Numbers
Bayesian optimization is a powerful tool for expensive stochastic black-box optimization problems such as simulation-based optimization or machine learning hyperparameter tuning.
Dynamic Subgoal-based Exploration via Bayesian Optimization
Reinforcement learning in sparse-reward navigation environments with expensive and limited interactions is challenging and poses a need for effective exploration.
Scalable Global Optimization via Local Bayesian Optimization
This motivates the design of a local probabilistic approach for global optimization of large-scale high-dimensional problems.
Bayesian Optimization of Combinatorial Structures
The optimization of expensive-to-evaluate black-box functions over combinatorial structures is an ubiquitous task in machine learning, engineering and the natural sciences.
Bayesian Optimization with Gradients
Bayesian optimization has been successful at global optimization of expensive-to-evaluate multimodal objective functions.
Warm Starting Bayesian Optimization
We develop a framework for warm-starting Bayesian optimization, that reduces the solution time required to solve an optimization problem that is one in a sequence of related problems.
Multi-Information Source Optimization
We consider Bayesian optimization of an expensive-to-evaluate black-box objective function, where we also have access to cheaper approximations of the objective.
