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Scalable Inference for Structured Gaussian Process Models
"... This thesis contributes to the field of Bayesian machine learning. Familiarity with most of the material in Bishop [2007], MacKay [2003] and Hastie et al. [2009] would thus be convenient for the reader. Sections which may be skipped by the expert reader without disrupting the flow of the text have b ..."
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Cited by 7 (2 self)
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This thesis contributes to the field of Bayesian machine learning. Familiarity with most of the material in Bishop [2007], MacKay [2003] and Hastie et al. [2009] would thus be convenient for the reader. Sections which may be skipped by the expert reader without disrupting the flow of the text have
Generalised Bayesian Matrix Factorisation Models
"... I, SHAKIR MOHAMED, confirm that this dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except where specifically indicated in the text. Where information has been derived from other sources, I confirm that this has been indicated in the ..."
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I, SHAKIR MOHAMED, confirm that this dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except where specifically indicated in the text. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. I also confirm that this thesis is below 60,000 words and contains less than 150 figures, in fulfilment of the requirements set by the degree committee for the Department of Engineering at the University of Cambridge. iii Factor analysis and related models for probabilistic matrix factorisation are of central importance to the unsupervised analysis of data, with a colourful history more than a century long. Probabilistic models for matrix factorisation allow us to explore the underlying structure in data, and have relevance in a vast number of application areas including collaborative filtering, source separation, missing data imputation, gene expression analysis, information retrieval, computational finance and computer vision, amongst others. This thesis develops generalisations of matrix
Bayesian Reasoning and Machine Learning
"... V a calligraphic symbol typically denotes a set of random variables........ 7 dom(x) Domain of a variable.................................................... 7 x = x The variable x is in the state x.......................................... 7 p(x = tr) probability of event/variable x being in the st ..."
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V a calligraphic symbol typically denotes a set of random variables........ 7 dom(x) Domain of a variable.................................................... 7 x = x The variable x is in the state x.......................................... 7 p(x = tr) probability of event/variable x being in the state true................... 7 p(x = fa) probability of event/variable x being in the state false................... 7 p(x, y) probability of x and y................................................... 8 p(x ∩ y) probability of x and y................................................... 8 p(x ∪ y) probability of x or y.................................................... 8 p(xy) The probability of x conditioned on y................................... 8 X ⊥YZ Variables X are independent of variables Y conditioned on variables Z. 11 X ⊤YZ Variables X are dependent on variables Y conditioned on variables Z.. 11 x f(x) For continuous variables this is shorthand for ∫ f(x)dx and for discrete variables means summation over the states of x, ∑ x
 Student Consortium
, 2013
"... on Wirtschaftsinformatik. The copyrights of papers belong to the authors. The papers reflect the authors ’ opinions, and in the interests of timely dissemination, are published without change. Their inclusion in this book does not necessarily constitute endorsement by the editors. The editors have n ..."
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on Wirtschaftsinformatik. The copyrights of papers belong to the authors. The papers reflect the authors ’ opinions, and in the interests of timely dissemination, are published without change. Their inclusion in this book does not necessarily constitute endorsement by the editors. The editors have
Scalable Movement Representation In Low Dimensional Latent Space
"... We investigate a novel approach for representation of kinematic trajectories in complex movement systems. Our framework consists of a powerful nonlinear, probabilistic and fully generative dimensionality reduction method coupled with a nonlinear dynamical systemsbased motion representation techniq ..."
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dimensional latent space requires only a small number of systems, without loss of accuracy with respect to the demonstrated motion. This way the encoding is made more compact and more manageable. Moreover, the modification of parameters of the motion representation in latent space is demonstrated to lead
Learning decisions: Robustness, uncertainty, and approximation
, 2004
"... Decision making under uncertainty is a central problem in robotics and machine learning. This thesis explores three fundamental and intertwined aspects of the problem of learning to make decisions. The first is the problem of uncertainty. Classical optimal control techniques typically rely on perfec ..."
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Cited by 13 (3 self)
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. It is important to ensure that decision policies we generate are robust both to uncertainty in our models of systems and to our inability to accurately capture true system dynamics. We present new classes of algorithms that gracefully handle uncertainty, approximation,
Global Optimization Algorithms  Theory and Application
, 2011
"... This ebook is devoted to Global Optimization algorithms, which are methods for finding solutions of high quality for an incredible wide range of problems. We introduce the basic concepts of optimization and discuss features which make optimization problems difficult and thus, should be considered ..."
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Cited by 94 (26 self)
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This ebook is devoted to Global Optimization algorithms, which are methods for finding solutions of high quality for an incredible wide range of problems. We introduce the basic concepts of optimization and discuss features which make optimization problems difficult and thus, should be considered when trying to solve them. In this book, we focus on
Sources En Imagerie Acoustique Bayesian Approach In Acoustic Source Localization And Imaging
"... te l0 ..."
Taking the Human Out of the Loop: A Review of Bayesian Optimization
"... Big data applications are typically associated with systems involving large numbers of users, massive complex software systems, and largescale heterogeneous computing and storage architectures. The construction of such systems involves many distributed design choices. The end products (e.g., reco ..."
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improvements. Bayesian optimization is a powerful tool for the joint optimization of design choices that is gaining great popularity in recent years. It promises greater automation so as to increase both product quality and human productivity. This review paper introduces Bayesian optimization, highlights some
PolicyGradient Algorithms for Partially Observable Markov decision processes
, 2003
"... Partially observable Markov decision processes are interesting because of their ability to model most conceivable realworld learning problems, for example, robot navigation, driving a car, speech recognition, stock trading, and playing games. The downside of this generality is that exact algorithms ..."
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Cited by 36 (2 self)
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of a reward signal. Policygradient methods are attractive as a scalable approach for controlling partially observable Markov decision processes (POMDPs). In the most
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