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Extreme Multi Label Learning With Gaussian Processes
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Book Synopsis Extreme Multi-label Learning with Gaussian Processes by : Aristeidis Panos
Download or read book Extreme Multi-label Learning with Gaussian Processes written by Aristeidis Panos and published by . This book was released on 2019 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: In modern probabilistic machine learning, Gaussian process models have provided both powerful and principled ways to approach a series of challenging problems. Nonetheless, their applicability can be significantly limited by cases where the number of training data points is large, something very typical in many modern machine learning applications. An additional restriction can be imposed when the posterior distribution is intractable due to non-Gaussian likelihoods used. Despite the fact that these two limitations have been efficiently addressed over the last decade, applications of Gaussian process models under extreme regimes where the number of the training data points and the dimensionality of both input and output space is extremely large have not appeared in literature so far. This thesis is focused on this kind of applications of Gaussian processes where supervised tasks such as multi-class and multi-label classification are considered. We start by discussing the main mathematical tools required in order to successfully cope with the large scale of the datasets. Those include a variational inference framework, suitably tailored for Gaussian processes. Furthermore, in our attempt to alleviate the computational burden, we introduce a new parametrization for the variational distribution while a representation trick for reducing storage requirements for large input dimensions is also discussed. A methodology is then presented which is based on this variational inference framework and a computationally efficient bound on the softmax function that allows the use of Gaussian processes for multi-class classification problems that involve arbitrarily large number of classes. A series of experiments test and compare the performance of this methodology with other methods. Finally, we move to the more general multi-label classification task and we develop a method, also relied on the same variational inference framework, which can deal with datasets involving hundreds of thousands data points, input dimensions and labels. The effectiveness of our method is supported by experiments on several real-world multi-label datasets.
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 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 Multi-Label Dimensionality Reduction by : Liang Sun
Download or read book Multi-Label Dimensionality Reduction written by Liang Sun and published by CRC Press. This book was released on 2016-04-19 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Similar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality. An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant, redundant, and noisy information. The data mining and machine learning literature currently lacks
Book Synopsis Multilabel Classification by : Francisco Herrera
Download or read book Multilabel Classification written by Francisco Herrera and published by Springer. This book was released on 2016-08-09 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a comprehensive review of multilabel techniques widely used to classify and label texts, pictures, videos and music in the Internet. A deep review of the specialized literature on the field includes the available software needed to work with this kind of data. It provides the user with the software tools needed to deal with multilabel data, as well as step by step instruction on how to use them. The main topics covered are: • The special characteristics of multi-labeled data and the metrics available to measure them.• The importance of taking advantage of label correlations to improve the results.• The different approaches followed to face multi-label classification.• The preprocessing techniques applicable to multi-label datasets.• The available software tools to work with multi-label data. This book is beneficial for professionals and researchers in a variety of fields because of the wide range of potential applications for multilabel classification. Besides its multiple applications to classify different types of online information, it is also useful in many other areas, such as genomics and biology. No previous knowledge about the subject is required. The book introduces all the needed concepts to understand multilabel data characterization, treatment and evaluation.
Book Synopsis Structural, Syntactic, and Statistical Pattern Recognition by : Xiao Bai
Download or read book Structural, Syntactic, and Statistical Pattern Recognition written by Xiao Bai and published by Springer. This book was released on 2018-08-10 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2018, held in Beijing, China, in August 2018. The 49 papers presented in this volume were carefully reviewed and selected from 75 submissions. They were organized in topical sections named: classification and clustering; deep learning and neurla networks; dissimilarity representations and Gaussian processes; semi and fully supervised learning methods; spatio-temporal pattern recognition and shape analysis; structural matching; multimedia analysis and understanding; and graph-theoretic methods.
Book Synopsis Kernels for Vector-Valued Functions by : Mauricio A. Álvarez
Download or read book Kernels for Vector-Valued Functions written by Mauricio A. Álvarez and published by Foundations & Trends. This book was released on 2012 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph reviews different methods to design or learn valid kernel functions for multiple outputs, paying particular attention to the connection between probabilistic and regularization methods.
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 Learning Kernel Classifiers by : Ralf Herbrich
Download or read book Learning Kernel Classifiers written by Ralf Herbrich and published by MIT Press. This book was released on 2022-11-01 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier—a limited, but well-established and comprehensively studied model—and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
Book Synopsis Graphical Models for Machine Learning and Digital Communication by : Brendan J. Frey
Download or read book Graphical Models for Machine Learning and Digital Communication written by Brendan J. Frey and published by MIT Press. This book was released on 1998 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Content Description. #Includes bibliographical references and index.
Book Synopsis Multi-task Learning with Gaussian Processes by : Kian Ming Adam Chai
Download or read book Multi-task Learning with Gaussian Processes written by Kian Ming Adam Chai and published by . This book was released on 2010 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Automated Machine Learning by : Frank Hutter
Download or read book Automated Machine Learning written by Frank Hutter and published by Springer. This book was released on 2019-05-17 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Ensemble Multi-label Learning in Supervised and Semi-supervised Settings by : Ouadie Gharroudi
Download or read book Ensemble Multi-label Learning in Supervised and Semi-supervised Settings written by Ouadie Gharroudi and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-label learning is a specific supervised learning problem where each instance can be associated with multiple target labels simultaneously. Multi-label learning is ubiquitous in machine learning and arises naturally in many real-world applications such as document classification, automatic music tagging and image annotation. In this thesis, we formulate the multi-label learning as an ensemble learning problem in order to provide satisfactory solutions for both the multi-label classification and the feature selection tasks, while being consistent with respect to any type of objective loss function. We first discuss why the state-of-the art single multi-label algorithms using an effective committee of multi-label models suffer from certain practical drawbacks. We then propose a novel strategy to build and aggregate k-labelsets based committee in the context of ensemble multi-label classification. We then analyze the effect of the aggregation step within ensemble multi-label approaches in depth and investigate how this aggregation impacts the prediction performances with respect to the objective multi-label loss metric. We then address the specific problem of identifying relevant subsets of features - among potentially irrelevant and redundant features - in the multi-label context based on the ensemble paradigm. Three wrapper multi-label feature selection methods based on the Random Forest paradigm are proposed. These methods differ in the way they consider label dependence within the feature selection process. Finally, we extend the multi-label classification and feature selection problems to the semi-supervised setting and consider the situation where only few labelled instances are available. We propose a new semi-supervised multi-label feature selection approach based on the ensemble paradigm. The proposed model combines ideas from co-training and multi-label k-labelsets committee construction in tandem with an inner out-of-bag label feature importance evaluation. Satisfactorily tested on several benchmark data, the approaches developed in this thesis show promise for a variety of applications in supervised and semi-supervised multi-label learning.
Book Synopsis Gaussian Processes at Extreme Lengthscales by : Ryan-Rhys Griffiths
Download or read book Gaussian Processes at Extreme Lengthscales written by Ryan-Rhys Griffiths and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Gaussian Processes Based Transfer Learning for Online Multiple-person Tracking and Building Blocks for Deep Learning by : Baobing Zhang
Download or read book Gaussian Processes Based Transfer Learning for Online Multiple-person Tracking and Building Blocks for Deep Learning written by Baobing Zhang and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Algorithmic Learning Theory by : Peter Auer
Download or read book Algorithmic Learning Theory written by Peter Auer and published by Springer. This book was released on 2014-10-01 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 25th International Conference on Algorithmic Learning Theory, ALT 2014, held in Bled, Slovenia, in October 2014, and co-located with the 17th International Conference on Discovery Science, DS 2014. The 21 papers presented in this volume were carefully reviewed and selected from 50 submissions. In addition the book contains 4 full papers summarizing the invited talks. The papers are organized in topical sections named: inductive inference; exact learning from queries; reinforcement learning; online learning and learning with bandit information; statistical learning theory; privacy, clustering, MDL, and Kolmogorov complexity.
Book Synopsis Graph Representation Learning by : William L. William L. Hamilton
Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
