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Inference About Expert Beliefs Using A Gaussian Process Model
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Book Synopsis Inference about Expert Beliefs Using a Gaussian Process Model by : John Paul Gosling
Download or read book Inference about Expert Beliefs Using a Gaussian Process Model written by John Paul Gosling and published by . This book was released on 2006 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Gaussian Processes for Machine Learning by : Carl Edward Rasmussen
Download or read book Gaussian Processes for Machine Learning written by Carl Edward Rasmussen and published by MIT Press. This book was released on 2005-11-23 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
Book Synopsis Scalable and Automated Inference for Gaussian Process Models by : Trung Van Nguyen
Download or read book Scalable and Automated Inference for Gaussian Process Models written by Trung Van Nguyen and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gaussian processes (GPs) are widely used in the Bayesian approach to supervised learning. Their ability to provide rich priors over functions is highly desirable for modeling real-world problems. Unfortunately, there exist two big challenges when doing Bayesian inference (i.e., learning the posteriors over functions) for GP models. The first is analytical intractability: The posteriors cannot be computed in closed- form when non-Gaussian likelihoods are employed. The second is scalability: The inference procedures often cannot be applied to large datasets due to their prohibitive computational costs. In this thesis, I develop practical variational inference methods to address the first challenge. Moreover, I introduce three GP models to deal with the second challenge. First, I focus on the analytical intractability challenge starting with the Gaussian process regression networks (GPRN), an expressive multi-output model with adaptive, input-dependent correlations. I derive a variational inference method with two different variational distributions to approximate the true posterior of GPRN. While one distribution is a standard Gaussian, the other is a Gaussian mixture which can capture more complex, multimodal posteriors. Both distributions are shown to be statistically efficient, requiring only a linear number of parameters to represent their inherent covariance matrices. Experimental results demonstrate clear benefits of having a multimodal variational approximation in GPRN. Next, I use the same two variational distributions to address the analytical in- tractability challenge for a large class of GP models. I show that the aforementioned statistical efficiency also stands for members of this class. I further prove that the gradients required for variational learning can either be approximated efficiently or computed analytically, regardless of the likelihood functions of the models. Based on these insights, I develop an automated variational inference method for GP models with general likelihoods. The method allows easy investigation of existing or new models without having to derive model-specific inference algorithms. I then turn to the scalability challenge, focusing on single-output and multi- output regression. The underpinning technique here is sparse GP - a GP augmented with so-called inducing points/variables that lead to lower computational demands. For single-output regression, I introduce a mixture-of-experts model (FGP) where the experts are independent sparse GPs each having their own inducing variables. Their inducing inputs further define a partitioning structure of the input space, allowing an efficient inference scheme in which computation is carried out locally by the experts. FGP can thus be K2 time faster and use K2 less memory than previous GP models, where K is the number of experts. For multi-output regression, I introduce the collaborative multi-output Gaussian process model (COGP) where the outputs are linear combinations of independent sparse GPs. Their inducing points are represented as global variables which correlate the outputs for joint learning. The variables are then exploited to derive a stochastic variational inference method that can deal with a much larger number of inputs and outputs compared to previous models. Superior empirical performance of FGP and COGP is demonstrated through extensive experiments on various real-world datasets.
Book Synopsis Efficient Reinforcement Learning Using Gaussian Processes by : Marc Peter Deisenroth
Download or read book Efficient Reinforcement Learning Using Gaussian Processes written by Marc Peter Deisenroth and published by KIT Scientific Publishing. This book was released on 2010 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference in nonlinear dynamic systems.First, we introduce PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action spaces when no expert knowledge is available. PILCO takes model uncertainties consistently into account during long-term planning to reduce model bias. Second, we propose principled algorithms for robust filtering and smoothing in GP dynamic systems.
Book Synopsis Scalable Inference for Structured Gaussian Process Models by : Yunus Saatçi
Download or read book Scalable Inference for Structured Gaussian Process Models written by Yunus Saatçi and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Automating Active Learning for Gaussian Processes by : Gustavo Malkomes
Download or read book Automating Active Learning for Gaussian Processes written by Gustavo Malkomes and published by . This book was released on 2019 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: In many problems in science, technology, and engineering, unlabeled data is abundant but acquiring labeled observations is expensive -- it requires a human annotator, a costly laboratory experiment, or a time-consuming computer simulation. Active learning is a machine learning paradigm designed to minimize the cost of obtaining labeled data by carefully selecting which new data should be gathered next. However, excessive machine learning expertise is often required to effectively apply these techniques in their current form. In this dissertation, we propose solutions that further automate active learning. Our core contributions are active learning algorithms that are easy for non-experts to use but that deliver results competitive with or better than human-expert solutions. We begin introducing a novel active search algorithm that automatically and dynamically balances exploration against exploitation --- without relying on a parameter to control this tradeoff. We also provide a theoretical investigation on the hardness of this problem, proving that no polynomial-time policy can achieve a constant factor approximation ratio for the expected utility of the optimal policy. Next, we introduce a novel information-theoretic approach for active model selection. Our method is based on maximizing the mutual information between the output variable and the model class. This is the first active-model-selection approach that does not require updating each model for every candidate point. As a result, we successfully developed an automated audiometry test for rapid screening of noise-induced hearing loss, a widespread and preventable disability, if diagnosed early. We proceed by introducing a novel model selection algorithm for fixed-size datasets, called Bayesian optimization for model selection (BOMS). Our proposed model search method is based on Bayesian optimization in model space, where we reason about the model evidence as a function to be maximized. BOMS is capable of finding a model that explains the dataset well without any human assistance. Finally, we extend BOMS to active learning, creating a fully automatic active learning framework. We apply this framework to Bayesian optimization, creating a sample-efficient automated system for black-box optimization. Crucially, we account for the uncertainty in the choice of model; our method uses multiple and carefully-selected models to represent its current belief about the latent objective function.Our algorithms are completely general and can be extended to any class of probabilistic models. In this dissertation, however, we mainly use the powerful class of Gaussian process models to perform inference. Extensive experimental evidence is provided to demonstrate that all proposed algorithms outperform previously developed solutions to these problems.
Book Synopsis Scalable Gaussian Process Inference Using Variational Methods by : Alexander Graeme de Garis Matthews
Download or read book Scalable Gaussian Process Inference Using Variational Methods written by Alexander Graeme de Garis Matthews and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Scalable Approximate Inference and Model Selection in Gaussian Process Regression by : David Burt
Download or read book Scalable Approximate Inference and Model Selection in Gaussian Process Regression written by David Burt and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Variational Inference for Composite Gaussian Process Models by : Jakob Lindinger
Download or read book Variational Inference for Composite Gaussian Process Models written by Jakob Lindinger and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Intention Inference and Decision Making with Hierarchical Gaussian Process Dynamics Models by : Zhikun Wang
Download or read book Intention Inference and Decision Making with Hierarchical Gaussian Process Dynamics Models written by Zhikun Wang and published by . This book was released on 2013 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Mixture of expert models. Statistical analysis method by : Jula Kabeto Bunkure
Download or read book Mixture of expert models. Statistical analysis method written by Jula Kabeto Bunkure and published by GRIN Verlag. This book was released on 2020-06-16 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt: Document from the year 2020 in the subject Mathematics - Statistics, grade: Book chapter, Bahir Dar University (Ethiopian Institute of Textile and fashion technology), course: Statistical analysis method, language: English, abstract: Mixtures of experts models consist of a set of experts, which model conditional probabilistic processes, and a gate which combines the probabilities of the experts. The probabilistic basis for the mixture of experts is that of a mixture model in which the experts form the input conditional mixture components while the gate outputs form the input conditional mixture weights. A straightforward generalisation of ME models is the hierarchical mixtures of experts (HME) class of models, in which each expert is made up of a mixture of experts in a recursive fashion. This principle states that complex problems can be better solved by decomposing them into smaller tasks. In mixtures of experts the assumption is that there are separate processes in the underlying process of generating the data. Modelling of these processes is performed by the experts while the decision of which process to use is modelled by the gate. Mixtures of experts have many connections with other algorithms such as tree-based methods, mixture models and switching regression. In this, I review the paper by Rasmussen and Ghahramani to see how closely the mixtures of experts model resembles these other algorithms, and what is novel about it. The aim of this review is to adopt the method used in the current article to local precipitation data.
Book Synopsis Scalable Inference in Latent Gaussian Process Models by : Florian Wenzel
Download or read book Scalable Inference in Latent Gaussian Process Models written by Florian Wenzel and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis AI 2020: Advances in Artificial Intelligence by : Marcus Gallagher
Download or read book AI 2020: Advances in Artificial Intelligence written by Marcus Gallagher and published by Springer Nature. This book was released on 2020-11-27 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 33rd Australasian Joint Conference on Artificial Intelligence, AI 2020, held in Canberra, ACT, Australia, in November 2020.* The 36 full papers presented in this volume were carefully reviewed and selected from 57 submissions. The paper were organized in topical sections named: applications; evolutionary computation; fairness and ethics; games and swarms; and machine learning. *The conference was held virtually due to the COVID-19 pandemic.
Book Synopsis Gaussian Process Deep Belief Networks by : Alessandro Di Martino
Download or read book Gaussian Process Deep Belief Networks written by Alessandro Di Martino and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis High Performance Computing by : Juan Luis Crespo-Mariño
Download or read book High Performance Computing written by Juan Luis Crespo-Mariño and published by Springer Nature. This book was released on 2020-02-12 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 6th Latin American High Performance Computing Conference, CARLA 2019, held in Turrialba, Costa Rica, in September 2019. The 32 revised full papers presented were carefully reviewed and selected out of 62 submissions. The papers included in this book are organized according to the conference tracks - regular track on high performance computing: applications; algorithms and models; architectures and infrastructures; and special track on bioinspired processing (BIP): neural and evolutionary approaches; image and signal processing; biodiversity informatics and computational biology.
Download or read book Ecological Inference written by Gary King and published by Cambridge University Press. This book was released on 2004-09-13 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Drawing upon the recent explosion of research in the field, a diverse group of scholars surveys the latest strategies for solving ecological inference problems, the process of trying to infer individual behavior from aggregate data. The uncertainties and information lost in aggregation make ecological inference one of the most difficult areas of statistical inference, but these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, marketing research by business, and policy analysis by governments. This wide-ranging collection of essays offers many fresh and important contributions to the study of ecological inference.
Book Synopsis Strategic Engineering for Cloud Computing and Big Data Analytics by : Amin Hosseinian-Far
Download or read book Strategic Engineering for Cloud Computing and Big Data Analytics written by Amin Hosseinian-Far and published by Springer. This book was released on 2017-02-13 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book demonstrates the use of a wide range of strategic engineering concepts, theories and applied case studies to improve the safety, security and sustainability of complex and large-scale engineering and computer systems. It first details the concepts of system design, life cycle, impact assessment and security to show how these ideas can be brought to bear on the modeling, analysis and design of information systems with a focused view on cloud-computing systems and big data analytics. This informative book is a valuable resource for graduate students, researchers and industry-based practitioners working in engineering, information and business systems as well as strategy.