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 14 of 14 for “"Sparse learning"”.
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Nonparametric sparse learning of dynamical systems
… we develop a nonparametric approach to learning the system dynamics via transfer operators in reproducing kernel Hilbert spaces (RKHS). Compared with methods using fixed parametric structures, the proposed nonparametric representation does not require manually engineered features, and …
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Sparse learning : statistical and optimization perspectives
… computational and statistical aspects of several sparse models when the number of samples and/or features is large. We propose new statistical estimators and build new computational algorithms - borrowing tools and techniques from areas of convex and discrete optimization. First, we explore an …
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Sparse Learning of Nonlinear PDE Dynamics using Kalman Smoothing
… the data, either implicitly or explicitly. The Sparse Identification of Nonlinear Dynamics (SINDy) method achieves this in two steps: a derivative estimation and smoothing step, followed by sparse regression over a library of candidate functions. Previous implementations of the derivative step …
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Sparse Learning using Discrete Optimization: Scalable Algorithms and Statistical Insights
… is a central concept in interpretable machine learning and high-dimensional statistics. While sparse learning problems can be naturally modeled using discrete optimization, computational challenges have historically shifted the focus towards alternatives based on continuous optimization and …
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Efficient Sparse Bayesian Learning using Spike-and-Slab Priors
In the context of statistical machine learning, sparse learning is a procedure that seeks a reconciliation between two competing aspects of a statistical model: good predictive power and interpretability. In a Bayesian setting, sparse learning methods invoke sparsity inducing priors to explicitly …
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High-Dimensional Generative Models for 3D Perception
… of high-dimensional frameworks for data learning. Here, we design several sparse learning-based approaches for high-dimensional data that effectively tackle multiple perception problems, including data filtering, data recovery, and data retrieval. The frameworks offer generative solutions …
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Optimization Methods for Machine Learning under Structural Constraints
In modern statistical and machine learning models, structural constraints are usually imposed for model interpretability as well as model complexity reduction. In this thesis, we present scalable optimization methods for several large-scale machine learning problems under structural constraints, …
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Regression under a modern optimization lens
… demonstrate that our approach outperforms other sparse learning procedures. In Part II of the thesis, we build off of Part I to modify the objective function and include constraints that will produce linear regression models with other desirable properties, in addition to sparsity. We develop a …
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Gaussian processes:iterative sparse approximations
… parametric models. These methods use Bayesian learning, which generally leads to analytically intractable posteriors. This thesis proposes a two-step solution to construct a probabilistic approximation to the posterior. In the first step we adapt the Bayesian online learning to GPs: the final …
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Generalised Bayesian matrix factorisation models
… We place emphasis on Bayesian approaches to learning and the advantages that come with the Bayesian methodology. Our port of departure is a generalisation of latent variable models to members of the exponential family of distributions. This generalisation allows for the analysis of data that …
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Big Data Phylogenomics: Methods and Applications
… (3) Development of a supervised machine learning approach based on the Evolutionary Sparse Learning framework for detecting fragile clades and associated gene-species combinations. This approach first builds a genetic model for a monophyletic clade of interest, clade probability for the …
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Sparse Value Function Approximation for Reinforcement Learning
<p>A key component of many reinforcement learning (RL) algorithms is the approximation of the value function. The design and selection of features for approximation in RL is crucial, and an ongoing area of research. One approach to the problem of feature selection is to apply sparsity-inducing …
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Learning in Human and Robot Search: Subgoal, Submodularity, and Sparsity
… search analysis, problem reformulation, and learning in robot search. In the first part, the goal is to analyze and model human search behavior. In the experiments, human subjects remotely control a mobile robot to search for a target through teleoperation. Their search data are collected for …
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Machine Learning Methods for Brain Image Analysis
… connectivity in the brain. I propose to use deep learning algorithms for the 2D segmentation of EM images. I designed an automated pipeline with novel insights that was able to achieve state-of-the-art performance on the segmentation of the \textit{Drosophila} brain. I also propose a novel …