Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 51 for “"structure learning"”.
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Scalable Structure Learning of Graphical Models
Hypothesis-free learning is increasingly popular given the large amounts of data becoming available. Structure learning, a hypothesis-free approach, of graphical models is a field of growing interest due to the power of such models and lack of domain knowledge when applied on complex real-world …
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Greedy structure learning of Markov Random Fields
… In this document we examine the problem of learning the structure of an undirected graphical model, also called a Markov Random Field (MRF), given a set of independent and identically distributed (i.i.d.) samples. Specifically, we introduce an adaptive forward-backward greedy algorithm for …
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Scalable sparsity structure learning using Bayesian methods
Learning sparsity pattern in high dimension is a great challenge in both implementation and theory. In this thesis we develop scalable Bayesian algorithms based on EM algorithm and variational inference to learn sparsity structure in various models. Estimation consistency and selection consistency …
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Structure learning in high-dimensional graphical models
… efficient and provably consistent algorithms for learning the structure of undirected and directed (causal) graphical models in the high-dimensional setting. Structure learning in graphical models is a central problem in statistics with numerous applications including learning gene regulatory …
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Causal Structure Learning through Double Machine Learning
Learning the causal structure of a system solely from observational data is a fundamental yet intricate task with numerous applications across various fields, including economics, earth sciences, biology, and medicine. This task is challenging due to several reasons: i) observational data alone, as …
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Problem dependent metaheuristic performance in Bayesian network structure learning.
Bayesian network (BN) structure learning from data has been an active research area in the machine learning field in recent decades. Much of the research has considered BN structure learning as an optimization problem. However, the finding of optimal BN from data is NP-hard. This fact has driven …
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Bayesian Active Structure Learning for Gaussian Process Probabilistic Programs
… should we gather to learn about the underlying structure of the world as quickly as possible, especially in cases where data is sparse or expensive to acquire? Structure learning techniques for Gaussian process (GP) probabilistic programs provide a rich framework for inferring qualitative …
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Latent tree structure learning for cross-document coreference resolution
… Coreference Resolution (CDCR) is the problem of learning which mentions, coming from several different documents, correspond to the same entity. This thesis approaches the CDCR problem by first turning it into a structure learning problem. A latent tree structure, in which leaves correspond to …
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Scalable Structure Learning, Inference, and Analysis with Probabilistic Programs
How can we automate and scale up the processes of learning accurate probabilistic models of complex data and obtaining principled solutions to probabilistic inference and analysis queries? This thesis presents efficient techniques for addressing these fundamental challenges grounded in …
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Data-Driven Localization and Structure Learning in Reverberant Underwater Acoustic Environments
… and tracking of a mobile emitter, and the joint learning of its reverberant 3D environment, are important yet challenging tasks in the shallow-water underwater acoustic setting. A typical application is the monitoring of submarines or other man-made emitters with a small, surreptitiously-deployed …
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Generalized Task Structure Learning for Collaborative Multi-Robot/Human-Robot Task Allocation
… concerns, we have developed a generalized task structure which is able to transfer skills of a learned task to teams of heterogeneous robots. This system uses a small number of human demonstrations to learn a hierarchical task structure on a single robot. This structure acts as a skeleton for …
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Bayesian Time Series Structure Learning: Formulation of an Event Driven Prior Distribution
We study the prior distribution over structures of a Bayesian time series structure learning model—the Temporal Interaction Model (TIM) of Siracusa and Fisher III. We develop a new method for setting the hyperparameters of the TIM structure prior. Our contribution enables more consistent inference …
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Individual Differences in Multilingualism and Language Learning: Effects on Cognitive Flexibility and Structure Learning
… Flexibility, Working Memory, Inhibition) and Structure Learning, a novel statistical learning framework involving learning under uncertainty. Furthermore, various age groups, including children, young and middle-aged adults have been assessed, as well as diverse sociolinguistic contexts …
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An Empirical Study on Sum-Product Networks Structure Learning and Deep Convolutional Sum-Product Networks
… for its tractability and easy methods for learning. Our rst main contribution is an empirical comparison of methods for SPN learning and inference. LearnSPN is a popular algorithm for learning SPNs that utilizes chop and slice operations. As g-test is a standard chopping method and Gaussian …
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An Approach for Fast Score Computation in Bayesian Network Structure Learning Over Large-Scale Distributed Data
… field is saturated with techniques to learn the structure of a Bayesian network (also known as Bayes network). Nevertheless, most of the techniques struggle when the number of variables (or network nodes) and the input data grow drastically. At that point, parallel distributed processing is the …
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Bayesian network structure learning using characteristic properties of permutation representations with applications to prostate cancer treatment.
… between variables by means of a node structure and a set of parameters. Learning efficiently the structure that models a particular dataset is a NP-hard task that requires substantial computational efforts to be successful. Although there exist many families of techniques for this …
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Causal structure discovery from incomplete data
Causal structure learning is a fundamental tool for building a scientific understanding of the way a system works. However, in many application areas, such as genomics, the information necessary for current causal structure learning algorithms does not match the information that researchers can …
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Geometric Deep Learning for Healthcare Applications
… Networks (GNNs), a subset of Geometric Deep Learning methods, for medical image analysis and causal structure learning. Tracking the progression of pathologies in chest radiography poses several challenges in anatomical motion estimation and image registration as this task requires spatially …
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