High-dimensional Bayesian optimization using low-dimensional feature spaces
نویسندگان
چکیده
منابع مشابه
High Dimensional Bayesian Optimization using Dropout
Scaling Bayesian optimization to high dimensions is challenging task as the global optimization of high-dimensional acquisition function can be expensive and often infeasible. Existing methods depend either on limited “active” variables or the additive form of the objective function. We propose a new method for high-dimensional Bayesian optimization, that uses a dropout strategy to optimize onl...
متن کاملBatched Large-scale Bayesian Optimization in High-dimensional Spaces
Bayesian optimization (BO) has become an effective approach for black-box function optimization problems when function evaluations are expensive and the optimum can be achieved within a relatively small number of queries. However, many cases, such as the ones with high-dimensional inputs, may require a much larger number of observations for optimization. Despite an abundance of observations tha...
متن کاملGeneralized Reinforcement Learning for Manipulation Skills – Combining Low-dimensional Bayesian Optimization with High-dimensional Motion Optimization
This paper addresses the problem of how a robot can autonomously improve a manipulation skill in an efficient and secure manner. Instead of using the standard reinforcement learning formulation where all objectives are defined in a single reward function, we propose a generalized formulation that consists of three components: 1) A known analytic cost function; 2) A black-box reward function; 3)...
متن کاملOptimal Positioning in low-Dimensional control Spaces using Convex Optimization
A music analysis and control system for use in live performance is presented and demonstrated. Musical features, such as harmony, rhythmic patterning, or melodic structure, are extracted and automatically placed at appropriate locations in a control space. The control space is of low dimensionality, usually only two dimensions, wherein perceptual dissimilarity is represented as distance. The sy...
متن کاملParticle Swarm Optimization in High Dimensional Spaces
Global optimization methods including Particle Swarm Optimization are usually used to solve optimization problems when the number of parameters is small (hundreds). In the case of inverse problems the objective (or fitness) function used for sampling requires the solution of multiple forward solves. In inverse problems, both a large number of parameters, and very costly forward evaluations hamp...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Machine Learning
سال: 2020
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-020-05899-z