Efficient Bayesian Hyperparameter Optimization - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Efficient Bayesian Hyperparameter Optimization write by Aaron Klein. This book was released on 2020. Efficient Bayesian Hyperparameter Optimization available in PDF, EPUB and Kindle. Abstract: Automated machine learning emerged as a new research field inside of machine learning that tries to progressively automate different steps of common machine learning pipelines which are traditionally executed by humans. One of its core tasks is the automated search for the right hyperparameters of a given machine learning algorithm which in practice is often essential to achieve good performance. Compare to other optimization problems, hyperparameter optimization is usually particularly expensive, since in each iteration, it requires to train and validate the underlying algorithm. One of the most successful approaches for hyperparameter optimization is Bayesian optimization. At its core, Bayesian optimization fits a probabilistic model of the objective function, which together with an additional acquisition function is used to guide the search towards the global optimum. In this thesis we present several extensions to standard Bayesian optimization to improve its performance for hyperparameter optimization problems. First, we introduce a new probabilistic model based on Bayesian neural networks, that allows to model the performance of hyperparameter configurations across different tasks and thereby scales much better with the number of data points and dimensions than Gaussian processes which are traditionally used inside Bayesian optimization. In hyperparameter optimization, often approximations, so-called fidelities, of the objective function are available which are much cheaper to evaluate. We present two new Bayesian optimization methods that can leverage such fidelities, such as learning curves or dataset subsets, to improve the overall search process in terms of wall-clock time by orders of magnitude. Furthermore, based on our proposed Bayesian neural network model, we present a new neural network architecture which models the learning curve of iterative machine learning methods, such as neural networks. Finally, due to the high computational cost of hyperparameter optimization, thorough benchmarking and evaluation of new developed methods is often prohibitively expensive. We show that one can approximate continuous and discrete benchmarks by surrogate benchmarks that capture the characteristics of the original benchmark but take only milliseconds to evaluate. This allows us to performa rigorous analysis and comparison of various different hyperparameter optimization methods from the literature
Hyperparameter Optimization in Machine Learning
Hyperparameter Optimization in Machine Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Hyperparameter Optimization in Machine Learning write by Tanay Agrawal. This book was released on 2021. Hyperparameter Optimization in Machine Learning available in PDF, EPUB and Kindle. Dive into hyperparameter tuning of machine learning models and focus on what hyperparameters are and how they work. This book discusses different techniques of hyperparameters tuning, from the basics to advanced methods. This is a step-by-step guide to hyperparameter optimization, starting with what hyperparameters are and how they affect different aspects of machine learning models. It then goes through some basic (brute force) algorithms of hyperparameter optimization. Further, the author addresses the problem of time and memory constraints, using distributed optimization methods. Next you'll discuss Bayesian optimization for hyperparameter search, which learns from its previous history. The book discusses different frameworks, such as Hyperopt and Optuna, which implements sequential model-based global optimization (SMBO) algorithms. During these discussions, you'll focus on different aspects such as creation of search spaces and distributed optimization of these libraries. Hyperparameter Optimization in Machine Learning creates an understanding of how these algorithms work and how you can use them in real-life data science problems. The final chapter summaries the role of hyperparameter optimization in automated machine learning and ends with a tutorial to create your own AutoML script. Hyperparameter optimization is tedious task, so sit back and let these algorithms do your work. You will: Discover how changes in hyperparameters affect the model's performance. Apply different hyperparameter tuning algorithms to data science problems Work with Bayesian optimization methods to create efficient machine learning and deep learning models Distribute hyperparameter optimization using a cluster of machines Approach automated machine learning using hyperparameter optimization.
Automated Machine Learning
Automated Machine Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Automated Machine Learning write by Frank Hutter. This book was released on 2019-05-17. Automated Machine Learning available in PDF, EPUB and Kindle. This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.
Automating Pareto-optimal Experiment Design Via Efficient Bayesian Optimization
Automating Pareto-optimal Experiment Design Via Efficient Bayesian Optimization - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Automating Pareto-optimal Experiment Design Via Efficient Bayesian Optimization write by Yunsheng Tian. This book was released on 2021. Automating Pareto-optimal Experiment Design Via Efficient Bayesian Optimization available in PDF, EPUB and Kindle. Many science, engineering, and design optimization problems require balancing the trade-offs between several conflicting objectives. The objectives are often blackbox functions whose evaluation requires time-consuming and costly experiments. Multi-objective Bayesian optimization can be used to automate the process of discovering the set of optimal solutions, called Pareto-optimal, while minimizing the number of performed evaluations. To further reduce the evaluation time in the optimization process, testing of several samples in parallel can be deployed. We propose DGEMO, a novel multi-objective Bayesian optimization algorithm that iteratively selects the best batch of samples to be evaluated in parallel. Our algorithm approximates and analyzes a piecewise-continuous Pareto set representation, which allows us to introduce a batch selection strategy that optimizes for both hypervolume improvement and diversity of selected samples in order to efficiently advance promising regions of the Pareto front. Experiments on both synthetic test functions and real-world benchmark problems show that our algorithm predominantly outperforms relevant state-of-the-art methods. The code is available at https://github.com/yunshengtian/DGEMO. In addition, we present AutoOED, an Optimal Experiment Design platform that implements several multi-objective Bayesian optimization algorithms with state-of-the-art performance including DGEMO with an intuitive graphical user interface (GUI). AutoOED is open-source and written in Python. The codebase is modular, facilitating extensions and tailoring the code, serving as a testbed for machine learning researchers to easily develop and evaluate their own multi-objective Bayesian optimization algorithms. Furthermore, a distributed system is integrated to enable parallelized experimental evaluations by independent workers in remote locations. The platform is available at https://autooed.org.
Bayesian Optimization and Data Science
Bayesian Optimization and Data Science - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Optimization and Data Science write by Francesco Archetti. This book was released on 2019-09-25. Bayesian Optimization and Data Science available in PDF, EPUB and Kindle. This volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization. It also analyzes the software resources available for BO and a few selected application areas. Some areas for which new results are shown include constrained optimization, safe optimization, and applied mathematics, specifically BO's use in solving difficult nonlinear mixed integer problems. The book will help bring readers to a full understanding of the basic Bayesian Optimization framework and gain an appreciation of its potential for emerging application areas. It will be of particular interest to the data science, computer science, optimization, and engineering communities.
