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 15 of 15 for “"Sparse Regression"”.
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Design Techniques for Efficient Sparse Regression Codes
Sparse regression codes (SPARCs) are a recently introduced coding scheme for the additive white Gaussian noise channel, for which polynomial time decoding algorithms have been proposed which provably achieve the Shannon channel capacity. One such algorithm is the approximate message passing (AMP) …
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Spatially Coupled Sparse Regression Codes for Single- and Multi-user Communications
Sparse regression codes (SPARCs) are a class of channel codes for efficient communication over the single-user additive white Gaussian noise (AWGN) channel at rates approaching the channel capacity. In a standard SPARC, codewords are sparse linear combinations of columns of an i.i.d. Gaussian …
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Model-Free Variable Screening, Sparse Regression Analysis and Other Applications with Optimal Transformations
… of the thesis, we develop a novel method called SParse Optimal Transformations (SPOT) to simultaneously select important variables and explore relationships between the response and predictor variables in high dimensional nonparametric regression analysis. Not only are the optimal transformations …
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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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From data to dynamics: discovering governing equations from data
… While machine learning approaches such as sparse regression and deep neural networks have been successful at discovering governing laws and reduced models from data, many challenges still remain. In this work, we focus on the discovery of nonlinear dynamical systems models from data. We …
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Geometric aspects of uncertainty quantification in high-dimensional statistics
… a geometric perspective. Specifically, under the sparse regression model, we layout results for foundational choices of loss functions while adding to the field by introducing the reweighted $\ell_2$ type of loss functions. The findings under the new class of loss functions not only unify some …
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Uncovering Perovskite Degradation Equations Using Scientific Machine Learning
… research has focused on optimization and regression over a limited training set. Inferring physical laws directly from data may allow the extraction of more generalizable scientific information that enables one to understand underlying mechanisms. In this study, we apply scientific ML — a …
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Progress on the Interplay of Machine Learning and Optimization
… under noise. Our method, which uses robust and sparse regression to build models of functions, is much more robust and has higher scalability than existing methods.
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Interpreting the role of non-coding genetic variation in human disease
… inference of hyperparameters in a structured sparse regression model. We propose an active sampling method to efficiently explore the hyperparameter space and avoid exponential scaling in the dimension of the hyperparameters. We show in simulation that our method can distinguish between …
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Algorithms for Large-scale Data Analytics and Applications to the COVID-19 Pandemic
… mixed-integer optimization problems including sparse regression, support vector machines, and the knapsack problem. We show that the algorithm is able to match or exceed state-of-the-art results. The second part of the thesis revolves around applying large-scale data analytics to the COVID-19 …
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Contributions to Data Reduction and Statistical Model of Data with Complex Structures
… of features. Traditional methods, such as linear regression or LASSO regression, cannot effectively deal with such a large dataset directly. This dissertation aims to develop several techniques to effectively analyze large datasets with complex structures in the observational, experimental and …
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Sparsity in Machine Learning: Theory and Applications
… It offers a rigorous framework to build sparse models and has proved to provide more accurate and sparse models than other approaches including the ones using sparsity-inducing regularization norms. This thesis focuses on the application of integer optimization to address sparsity …
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Learning Nonlinear Dynamics: Methods and Applications
… 3, we combine the approach from Chapter 2 with sparse regression to derive sparse ODEs from data, demonstrating enhanced robustness to observational noise. Our method shows improved performance in recovering the true structures and coefficients on canonical benchmark tests under significant …
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Learning and investigating phenomenological models for active matter
… leverages spectral basis representations and sparse regression algorithms to discover PDE models from microscopic simulation and experimental data, while incorporating the relevant physical symmetries. We illustrate the practical potential through applications to a chiral active particle model …
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Parallelisation of greedy algorithms for compressive sensing reconstruction
Compressive Sensing (CS) is a technique which allows a signal to be compressed at the same time as it is captured. The process of capturing and simultaneously compressing the signal is represented as linear sampling, which can encompass a variety of physical processes or signal processing. Instead …