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A Nonparametric Bayesian Perspective For Machine Learning In Partially Observed Settings
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Book Synopsis A Nonparametric Bayesian Perspective for Machine Learning in Partially-observed Settings by : Ferit Akova
Download or read book A Nonparametric Bayesian Perspective for Machine Learning in Partially-observed Settings written by Ferit Akova and published by . This book was released on 2013 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robustness and generalizability of supervised learning algorithms depend on the quality of the labeled data set in representing the real-life problem. In many real-world domains, however, we may not have full knowledge of the underlying data-generating mechanism, which may even have an evolving nature introducing new classes continually. This constitutes a partially-observed setting, where it would be impractical to obtain a labeled data set exhaustively defined by a fixed set of classes. Traditional supervised learning algorithms, assuming an exhaustive training library, would misclassify a future sample of an unobserved class with probability one, leading to an ill-defined classification problem. Our goal is to address situations where such assumption is violated by a non-exhaustive training library, which is a very realistic yet an overlooked issue in supervised learning. In this dissertation we pursue a new direction for supervised learning by defining self-adjusting models to relax the fixed model assumption imposed on classes and their distributions. We let the model adapt itself to the prospective data by dynamically adding new classes/components as data demand, which in turn gradually make the model more representative of the entire population. In this framework, we first employ suitably chosen nonparametric priors to model class distributions for observed as well as unobserved classes and then, utilize new inference methods to classify samples from observed classes and discover/model novel classes for those from unobserved classes. This thesis presents the initiating steps of an ongoing effort to address one of the most overlooked bottlenecks in supervised learning and indicates the potential for taking new perspectives in some of the most heavily studied areas of machine learning: novelty detection, online class discovery and semi-supervised learning.
Book Synopsis Bayesian Nonparametric Approaches for Reinforcement Learning in Partially Observable Domains by : Finale Doshi-Velez
Download or read book Bayesian Nonparametric Approaches for Reinforcement Learning in Partially Observable Domains written by Finale Doshi-Velez and published by . This book was released on 2012 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: Making intelligent decisions from incomplete information is critical in many applications: for example, medical decisions must often be made based on a few vital signs, without full knowledge of a patient's condition, and speech-based interfaces must infer a user's needs from noisy microphone inputs. What makes these tasks hard is that we do not even have a natural representation with which to model the task; we must learn about the task's properties while simultaneously performing the task. Learning a representation for a task also involves a trade-off between modeling the data that we have seen previously and being able to make predictions about new data streams. In this thesis, we explore one approach for learning representations of stochastic systems using Bayesian nonparametric statistics. Bayesian nonparametric methods allow the sophistication of a representation to scale gracefully with the complexity in the data. We show how the representations learned using Bayesian nonparametric methods result in better performance and interesting learned structure in three contexts related to reinforcement learning in partially-observable domains: learning partially observable Markov Decision processes, taking advantage of expert demonstrations, and learning complex hidden structures such as dynamic Bayesian networks. In each of these contexts, Bayesian nonparametric approach provide advantages in prediction quality and often computation time.
Book Synopsis Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection by : Xuefeng Zhou
Download or read book Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection written by Xuefeng Zhou and published by Springer Nature. This book was released on 2020-01-01 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book focuses on robot introspection, which has a direct impact on physical human-robot interaction and long-term autonomy, and which can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics, the ability to reason, solve their own anomalies and proactively enrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which can effectively be modeled as a parametric hidden Markov model (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using the hierarchical Dirichlet process (HDP) on the standard HMM parameters, known as the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states and allows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods. This book is a valuable reference resource for researchers and designers in the field of robot learning and multimodal perception, as well as for senior undergraduate and graduate university students.
Book Synopsis Bayesian Nonparametrics via Neural Networks by : Herbert K. H. Lee
Download or read book Bayesian Nonparametrics via Neural Networks written by Herbert K. H. Lee and published by SIAM. This book was released on 2004-01-01 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. Its goal is to demystify neural networks, putting them firmly in a statistical context rather than treating them as a black box. This approach is in contrast to existing books, which tend to treat neural networks as a machine learning algorithm instead of a statistical model. Once this underlying statistical model is recognized, other standard statistical techniques can be applied to improve the model. The Bayesian approach allows better accounting for uncertainty. This book covers uncertainty in model choice and methods to deal with this issue, exploring a number of ideas from statistics and machine learning. A detailed discussion on the choice of prior and new noninformative priors is included, along with a substantial literature review. Written for statisticians using statistical terminology, Bayesian Nonparametrics via Neural Networks will lead statisticians to an increased understanding of the neural network model and its applicability to real-world problems.
Book Synopsis Bayesian Analysis in Natural Language Processing, Second Edition by : Shay Cohen
Download or read book Bayesian Analysis in Natural Language Processing, Second Edition written by Shay Cohen and published by Springer Nature. This book was released on 2022-05-31 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: Natural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze language. Since then, the use of statistical techniques in NLP has evolved in several ways. One such example of evolution took place in the late 1990s or early 2000s, when full-fledged Bayesian machinery was introduced to NLP. This Bayesian approach to NLP has come to accommodate various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction examples. In this book, we cover the methods and algorithms that are needed to fluently read Bayesian learning papers in NLP and to do research in the area. These methods and algorithms are partially borrowed from both machine learning and statistics and are partially developed "in-house" in NLP. We cover inference techniques such as Markov chain Monte Carlo sampling and variational inference, Bayesian estimation, and nonparametric modeling. In response to rapid changes in the field, this second edition of the book includes a new chapter on representation learning and neural networks in the Bayesian context. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we review some of the fundamental modeling techniques in NLP, such as grammar modeling, neural networks and representation learning, and their use with Bayesian analysis.
Book Synopsis Bayesian Analysis in Natural Language Processing by : Shay Cohen
Download or read book Bayesian Analysis in Natural Language Processing written by Shay Cohen and published by Morgan & Claypool Publishers. This book was released on 2019-04-09 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Natural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze language. Since then, the use of statistical techniques in NLP has evolved in several ways. One such example of evolution took place in the late 1990s or early 2000s, when full-fledged Bayesian machinery was introduced to NLP. This Bayesian approach to NLP has come to accommodate various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction examples. In this book, we cover the methods and algorithms that are needed to fluently read Bayesian learning papers in NLP and to do research in the area. These methods and algorithms are partially borrowed from both machine learning and statistics and are partially developed "in-house" in NLP. We cover inference techniques such as Markov chain Monte Carlo sampling and variational inference, Bayesian estimation, and nonparametric modeling. In response to rapid changes in the field, this second edition of the book includes a new chapter on representation learning and neural networks in the Bayesian context. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we review some of the fundamental modeling techniques in NLP, such as grammar modeling, neural networks and representation learning, and their use with Bayesian analysis.
Book Synopsis Bayesian Analysis in Natural Language Processing by : Shay Cohen
Download or read book Bayesian Analysis in Natural Language Processing written by Shay Cohen and published by Springer Nature. This book was released on 2022-11-10 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Natural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze language. Since then, the use of statistical techniques in NLP has evolved in several ways. One such example of evolution took place in the late 1990s or early 2000s, when full-fledged Bayesian machinery was introduced to NLP. This Bayesian approach to NLP has come to accommodate for various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction examples. We cover the methods and algorithms that are needed to fluently read Bayesian learning papers in NLP and to do research in the area. These methods and algorithms are partially borrowed from both machine learning and statistics and are partially developed "in-house" in NLP. We cover inference techniques such as Markov chain Monte Carlo sampling and variational inference, Bayesian estimation, and nonparametric modeling. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we cover some of the fundamental modeling techniques in NLP, such as grammar modeling and their use with Bayesian analysis.
Book Synopsis Bayesian Reinforcement Learning by : Mohammad Ghavamzadeh
Download or read book Bayesian Reinforcement Learning written by Mohammad Ghavamzadeh and published by . This book was released on 2015-11-18 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. This monograph provides the reader with an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are that it provides an elegant approach to action-selection (exploration/exploitation) as a function of the uncertainty in learning, and it provides a machinery to incorporate prior knowledge into the algorithms. Bayesian Reinforcement Learning: A Survey first discusses models and methods for Bayesian inference in the simple single-step Bandit model. It then reviews the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model. It also presents Bayesian methods for model-free RL, where priors are expressed over the value function or policy class. Bayesian Reinforcement Learning: A Survey is a comprehensive reference for students and researchers with an interest in Bayesian RL algorithms and their theoretical and empirical properties.
Book Synopsis Nonparametric Bayesian Models for Unsupervised Learning by : Pu Wang
Download or read book Nonparametric Bayesian Models for Unsupervised Learning written by Pu Wang and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unsupervised learning is an important topic in machine learning. In particular, clustering is an unsupervised learning problem that arises in a variety of applications for data analysis and mining. Unfortunately, clustering is an ill-posed problem and, as such, a challenging one: no ground-truth that can be used to validate clustering results is available. Two issues arise as a consequence. Various clustering algorithms embed their own bias resulting from different optimization criteria. As a result, each algorithm may discover different patterns in a given dataset. The second issue concerns the setting of parameters. In clustering, parameter setting controls the characterization of individual clusters, and the total number of clusters in the data. Clustering ensembles have been proposed to address the issue of different biases induced by various algorithms. Clustering ensembles combine different clustering results, and can provide solutions that are robust against spurious elements in the data. Although clustering ensembles provide a significant advance, they do not address satisfactorily the model selection and the parameter tuning problem. Bayesian approaches have been applied to clustering to address the parameter tuning and model selection issues. Bayesian methods provide a principled way to address these problems by assuming prior distributions on model parameters. Prior distributions assign low probabilities to parameter values which are unlikely. Therefore they serve as regularizers for modeling parameters, and can help avoid over-fitting. In addition, the marginal likelihood is used by Bayesian approaches as the criterion for model selection. Although Bayesian methods provide a principled way to perform parameter tuning and model selection, the key question \How many clusters?" is still open. This is a fundamental question for model selection. A special kind of Bayesian methods, nonparametric Bayesian approaches, have been proposed to address this important model selection issue. Unlike parametric Bayesian models, for which the number of parameters is finite and fixed, nonparametric Bayesian models allow the number of parameters to grow with the number of observations. After observing the data, nonparametric Bayesian models t the data with finite dimensional parameters. An additional issue with clustering is high dimensionality. High-dimensional data pose a difficult challenge to the clustering process. A common scenario with high-dimensional data is that clusters may exist in different subspaces comprised of different combinations of features (dimensions). In other words, data points in a cluster may be similar to each other along a subset of dimensions, but not in all dimensions. People have proposed subspace clustering techniques, a.k.a. co-clustering or bi-clustering, to address the dimensionality issue (here, I use the term co-clustering). Like clustering, also co-clustering suffers from the ill-posed nature and the lack of ground-truth to validate the results. Although attempts have been made in the literature to address individually the major issues related to clustering, no previous work has addressed them jointly. In my dissertation I propose a unified framework that addresses all three issues at the same time. I designed a nonparametric Bayesian clustering ensemble (NBCE) approach, which assumes that multiple observed clustering results are generated from an unknown consensus clustering. The under- lying distribution is assumed to be a mixture distribution with a nonparametric Bayesian prior, i.e., a Dirichlet Process. The number of mixture components, a.k.a. the number of consensus clusters, is learned automatically. By combining the ensemble methodology and nonparametric Bayesian modeling, NBCE addresses both the ill-posed nature and the parameter setting/model selection issues of clustering. Furthermore, NBCE outperforms individual clustering methods, since it can escape local optima by combining multiple clustering results. I also designed a nonparametric Bayesian co-clustering ensemble (NBCCE) technique. NBCCE inherits the advantages of NBCE, and in addition it is effective with high dimensional data. As such, NBCCE provides a unified framework to address all the three aforementioned issues. NBCCE assumes that multiple observed co-clustering results are generated from an unknown consensus co-clustering. The underlying distribution is assumed to be a mixture with a nonparametric Bayesian prior. I developed two models to generate co-clusters in terms of row- and column- clusters. In one case row- and column-clusters are assumed to be independent, and NBCCE assumes two independent Dirichlet Process priors on the hidden consensus co-clustering, one for rows and one for columns. The second model captures the dependence between row- and column-clusters by assuming a Mondrian Process prior on the hidden consensus co-clustering. Combined with Mondrian priors, NBCCE provides more flexibility to fit the data. I have performed extensive evaluation on relational data and protein-molecule interaction data. The empirical evaluation demonstrates the effectiveness of NBCE and NBCCE and their advantages over traditional clustering and co-clustering methods.
Book Synopsis Bayesian Reasoning and Gaussian Processes for Machine Learning Applications by : Hemachandran K
Download or read book Bayesian Reasoning and Gaussian Processes for Machine Learning Applications written by Hemachandran K and published by CRC Press. This book was released on 2022-04-14 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas, such as game development, decision making, and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework. The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning, which is followed by Gaussian process introduction, classification, regression, covariance, and performance analysis of Gaussian processes with other models. FEATURES Contains recent advancements in machine learning Highlights applications of machine learning algorithms Offers both quantitative and qualitative research Includes numerous case studies This book is aimed at graduates, researchers, and professionals in the field of data science and machine learning.
Book Synopsis Reinforcement Learning by : Marco Wiering
Download or read book Reinforcement Learning written by Marco Wiering and published by Springer Science & Business Media. This book was released on 2012-03-05 with total page 653 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade. The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together they truly represent a state-of-the-art of current reinforcement learning research. Marco Wiering works at the artificial intelligence department of the University of Groningen in the Netherlands. He has published extensively on various reinforcement learning topics. Martijn van Otterlo works in the cognitive artificial intelligence group at the Radboud University Nijmegen in The Netherlands. He has mainly focused on expressive knowledge representation in reinforcement learning settings.
Book Synopsis Variational Bayesian Learning Theory by : Shinichi Nakajima
Download or read book Variational Bayesian Learning Theory written by Shinichi Nakajima and published by Cambridge University Press. This book was released on 2019-07-11 with total page 561 pages. Available in PDF, EPUB and Kindle. Book excerpt: Variational Bayesian learning is one of the most popular methods in machine learning. Designed for researchers and graduate students in machine learning, this book summarizes recent developments in the non-asymptotic and asymptotic theory of variational Bayesian learning and suggests how this theory can be applied in practice. The authors begin by developing a basic framework with a focus on conjugacy, which enables the reader to derive tractable algorithms. Next, it summarizes non-asymptotic theory, which, although limited in application to bilinear models, precisely describes the behavior of the variational Bayesian solution and reveals its sparsity inducing mechanism. Finally, the text summarizes asymptotic theory, which reveals phase transition phenomena depending on the prior setting, thus providing suggestions on how to set hyperparameters for particular purposes. Detailed derivations allow readers to follow along without prior knowledge of the mathematical techniques specific to Bayesian learning.
Book Synopsis Bayesian Nonparametrics by : Nils Lid Hjort
Download or read book Bayesian Nonparametrics written by Nils Lid Hjort and published by Cambridge University Press. This book was released on 2010-04-12 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics.
Book Synopsis Nonparametric Bayesian Models for Machine Learning by : Romain Jean Thibaux
Download or read book Nonparametric Bayesian Models for Machine Learning written by Romain Jean Thibaux and published by . This book was released on 2008 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis On Bayesian Inference for Partially Observed Data by : Roger Charles Gill
Download or read book On Bayesian Inference for Partially Observed Data written by Roger Charles Gill and published by . This book was released on 2007 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Nonparametric Probabilistic Methods in Machine Learning by : Justin C. Sahs
Download or read book Bayesian Nonparametric Probabilistic Methods in Machine Learning written by Justin C. Sahs and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Many aspects of modern science, business and engineering have become data-centric, relying on tools from Artificial Intelligence and Machine Learning. Practitioners and researchers in these fields need tools that can incorporate observed data into rich models of uncertainty to make discoveries and predictions. One area of study that provides such models is the field of Bayesian Nonparametrics. This dissertation is focused on furthering the development of this field. After reviewing the relevant background and surveying the field, we consider two areas of structured data: - We first consider relational data that takes the form of a 2-dimensional array--such as social network data. We introduce a novel nonparametric model that takes advantage of a representation theorem about arrays whose column and row order is unimportant. We then develop an inference algorithm for this model and evaluate it experimentally. - Second, we consider the classification of streaming data whose distribution evolves over time. We introduce a novel nonparametric model that finds and exploits a dynamic hierarchical structure underlying the data. We present an algorithm for inference in this model and show experimental results. We then extend our streaming model to handle the emergence of novel and recurrent classes, and evaluate the extended model experimentally.
Book Synopsis Nonparametric Perspectives on Empirical Bayes by : Nikolaos Ignatiadis
Download or read book Nonparametric Perspectives on Empirical Bayes written by Nikolaos Ignatiadis and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In an empirical Bayes analysis, we use data from repeated sampling to imitate inferences made by an oracle Bayesian with extensive knowledge of the data-generating distribution. Existing results provide a comprehensive characterization of when and why empirical Bayes point estimates accurately recover oracle Bayes behavior--in particular when the likelihood of the individual statistical problems is known and all problems are relevant to each other. In this thesis, we build upon advances in the theory of nonparametric statistics, machine learning, and computation to make three-fold contributions to the empirical Bayes literature: 1) We develop flexible and practical confidence intervals that provide asymptotic frequentist coverage of empirical Bayes estimands, such as the posterior mean or the local false sign rate. The coverage statements hold even when the estimands are only partially identified or when empirical Bayes point estimates converge very slowly. 2) We show that it is possible to achieve near-Bayes optimal mean squared error for the estimation of n effect sizes in the setting where both the prior and the per-problem likelihood are unknown. The requirement of our method is that we have access to replicated data, that is, each effect size of interest is estimated from K> 1 noisy observations. 3) We tackle the issue of relevance in empirical Bayes estimation of effect sizes. We propose a method that shrinks toward a per-problem location determined by a machine learning model prediction of the effect given side-information. We establish an extension to the classic result of James-Stein, whereby our proposed estimator dominates the sample mean for each problem under quadratic risk; even if the side-information contains no information about the true effects, or the machine learning model is arbitrarily miscalibrated. Taken together, these results broaden the applicability of empirical Bayes methods in areas such as genomics, and large scale experimentation, and demonstrate that it is fruitful to revisit traditional ideas in the empirical Bayes literature through a modern lens. The above results largely draw upon the following papers: Ignatiadis and Wager (2019, 2022) and Ignatiadis, Saha, Sun, and Muralidharan (2021).
