Bayesian optimization is a sequential design strategy for global optimization of black-box functions [1] [2] [3] that does not assume any functional forms. It is usually employed to optimize expensive-to-evaluate functions.
The term is generally attributed to Jonas Mockus and is coined in his work from a series of publications on global optimization in the 1970s and 1980s. [4] [5] [1]
Bayesian optimization is typically used on problems of the form , where is a set of points, , which rely upon less than 20 dimensions (), and whose membership can easily be evaluated. Bayesian optimization is particularly advantageous for problems where is difficult to evaluate due to its computational cost. The objective function, , is continuous and takes the form of some unknown structure, referred to as a "black box". Upon its evaluation, only is observed and its derivatives are not evaluated. [7]
Since the objective function is unknown, the Bayesian strategy is to treat it as a random function and place a prior over it. The prior captures beliefs about the behavior of the function. After gathering the function evaluations, which are treated as data, the prior is updated to form the posterior distribution over the objective function. The posterior distribution, in turn, is used to construct an acquisition function (often also referred to as infill sampling criteria) that determines the next query point.
There are several methods used to define the prior/posterior distribution over the objective function. The most common two methods use Gaussian processes in a method called kriging. Another less expensive method uses the Parzen-Tree Estimator to construct two distributions for 'high' and 'low' points, and then finds the location that maximizes the expected improvement. [8]
Standard Bayesian optimization relies upon each being easy to evaluate, and problems that deviate from this assumption are known as exotic Bayesian optimization problems. Optimization problems can become exotic if it is known that there is noise, the evaluations are being done in parallel, the quality of evaluations relies upon a tradeoff between difficulty and accuracy, the presence of random environmental conditions, or if the evaluation involves derivatives. [7]
Examples of acquisition functions include
and hybrids of these. [9] They all trade-off exploration and exploitation so as to minimize the number of function queries. As such, Bayesian optimization is well suited for functions that are expensive to evaluate.
The maximum of the acquisition function is typically found by resorting to discretization or by means of an auxiliary optimizer. Acquisition functions are typically well-behaved [ citation needed ] and are maximized using a numerical optimization technique, such as Newton's Method or quasi-Newton methods like the Broyden–Fletcher–Goldfarb–Shanno algorithm.
The approach has been applied to solve a wide range of problems, [10] including learning to rank, [11] computer graphics and visual design, [12] [13] [14] robotics, [15] [16] [17] [18] sensor networks, [19] [20] automatic algorithm configuration, [21] [22] automatic machine learning toolboxes, [23] [24] [25] reinforcement learning, [26] planning, visual attention, architecture configuration in deep learning, static program analysis, experimental particle physics, [27] [28] quality-diversity optimization, [29] [30] [31] chemistry, material design, and drug development. [7] [32] [33]
Bayesian Optimization has been applied in the field of facial recognition. [34] The performance of the Histogram of Oriented Gradients (HOG) algorithm, a popular feature extraction method, heavily relies on its parameter settings. Optimizing these parameters can be challenging but crucial for achieving high accuracy. [34] A novel approach to optimize the HOG algorithm parameters and image size for facial recognition using a Tree-structured Parzen Estimator (TPE) based Bayesian optimization technique has been proposed. [34] This optimized approach has the potential to be adapted for other computer vision applications and contributes to the ongoing development of hand-crafted parameter-based feature extraction algorithms in computer vision. [34]
Supervised learning (SL) is a paradigm in machine learning where input objects and a desired output value train a model. The training data is processed, building a function that maps new data on expected output values. An optimal scenario will allow for the algorithm to correctly determine output values for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way. This statistical quality of an algorithm is measured through the so-called generalization error.
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Global optimization is a branch of applied mathematics and numerical analysis that attempts to find the global minima or maxima of a function or a set of functions on a given set. It is usually described as a minimization problem because the maximization of the real-valued function is equivalent to the minimization of the function .
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Harold Joseph Kushner is an American applied mathematician and a Professor Emeritus of Applied Mathematics at Brown University. He is known for his work on the theory of stochastic stability, the theory of non-linear filtering, and for the development of numerical methods for stochastic control problems such as the Markov chain approximation method. He is commonly cited as the first person to study Bayesian optimization, based on work he published in 1964.
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The following outline is provided as an overview of and topical guide to machine learning:
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