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.
Efficient Bayesian Hyperparameter Optimization
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
Optimal Experimental Design for Large-scale Bayesian Inverse Problems
Optimal Experimental Design for Large-scale Bayesian Inverse Problems - 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 Optimal Experimental Design for Large-scale Bayesian Inverse Problems write by Keyi Wu (Ph. D.). This book was released on 2022. Optimal Experimental Design for Large-scale Bayesian Inverse Problems available in PDF, EPUB and Kindle. Bayesian optimal experimental design (BOED)—including active learning, Bayesian optimization, and sensor placement—provides a probabilistic framework to maximize the expected information gain (EIG) or mutual information (MI) for uncertain parameters or quantities of interest with limited experimental data. However, evaluating the EIG remains prohibitive for largescale complex models due to the need to compute double integrals with respect to both the parameter and data distributions. In this work, we develop a fast and scalable computational framework to solve Bayesian optimal experimental design (OED) problems governed by partial differential equations (PDEs) with application to optimal sensor placement by maximizing the EIG. We (1) exploit the low-rank structure of the Jacobian of the parameter-to-observable map to extract the intrinsic low-dimensional data-informed subspace, and (2) employ a series of approximations of the EIG that reduce the number of PDE solves while retaining a high correlation with the true EIG. This allows us to propose an efficient offline–online decomposition for the optimization problem, using a new swapping greedy algorithm for both OED problems and goal-oriented linear OED problems. The offline stage dominates the cost and entails precomputing all components requiring PDE solusion. The online stage optimizes sensor placement and does not require any PDE solves. We provide a detailed error analysis with an upper bound for the approximation error in evaluating the EIG for OED and goal-oriented OED linear cases. Finally, we evaluate the EIG with a derivative-informed projected neural network (DIPNet) surrogate for parameter-to-observable maps. With this surrogate, no further PDE solves are required to solve the optimization problem. We provided an analysis of the error propagated from the DIPNet approximation to the approximation of the normalization constant and the EIG under suitable assumptions. We demonstrate the efficiency and scalability of the proposed methods for both linear inverse problems, in which one seeks to infer the initial condition for an advection–diffusion equation, and nonlinear inverse problems, in which one seeks to infer coefficients for a Poisson problem, an acoustic Helmholtz problem and an advection–diffusion–reaction problem. This dissertation is based on the following articles: A fast and scalable computational framework for large-scale and high-dimensional Bayesian optimal experimental design by Keyi Wu, Peng Chen, and Omar Ghattas [88]; An efficient method for goal-oriented linear Bayesian optimal experimental design: Application to optimal sensor placement by Keyi Wu, Peng Chen, and Omar Ghattas [89]; and Derivative-informed projected neural network for large-scale Bayesian optimal experimental design by Keyi Wu, Thomas O’Leary-Roseberry, Peng Chen, and Omar Ghattas [90]. This material is based upon work partially funded by DOE ASCR DE-SC0019303 and DESC0021239, DOD MURI FA9550-21-1-0084, and NSF DMS-2012453
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.
Applications in Electronics Pervading Industry, Environment and Society
Applications in Electronics Pervading Industry, Environment and Society - 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 Applications in Electronics Pervading Industry, Environment and Society write by Riccardo Berta. This book was released on 2023-04-28. Applications in Electronics Pervading Industry, Environment and Society available in PDF, EPUB and Kindle. This book provides a thorough overview of cutting-edge research on electronics applications relevant to industry, the environment, and society at large. It covers a broad spectrum of application domains, from automotive to space and from health to security, while devoting special attention to the use of embedded devices and sensors for imaging, communication and control. The book is based on the 2022 ApplePies Conference, held in Genoa, Italy in September 2022, which brought together researchers and stakeholders to consider the most significant current trends in the field of applied electronics and to debate visions for the future. Areas addressed by the conference included information communication technology; biotechnology and biomedical imaging; space; secure, clean and efficient energy; the environment; and smart, green and integrated transport. As electronics technology continues to develop apace, constantly meeting previously unthinkable targets, further attention needs to be directed toward the electronics applications and the development of systems that facilitate human activities. This book, written by industrial and academic professionals, represents a valuable contribution in this endeavor.
