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You searched for +publisher:"Georgia Tech" +contributor:("Song, Le"). Showing records 1 – 30 of 33 total matches.

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Georgia Tech

1. Li, Qingbin. Online sufficient dimensionality reduction for sequential high-dimensional time-series.

Degree: MS, Industrial and Systems Engineering, 2015, Georgia Tech

In this thesis, we present Online Sufficient Dimensionality Reduction (OSDR) algorithm for real-time high-dimensional sequential data analysis. Advisors/Committee Members: Xie, Yao (advisor), Song, Le (committee member), Zhou, Enlu (committee member).

Subjects/Keywords: Online learning; Dimension reduction; Sufficient dimensionality reduction; Stochastic gradient descent

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APA (6th Edition):

Li, Q. (2015). Online sufficient dimensionality reduction for sequential high-dimensional time-series. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60385

Chicago Manual of Style (16th Edition):

Li, Qingbin. “Online sufficient dimensionality reduction for sequential high-dimensional time-series.” 2015. Masters Thesis, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/60385.

MLA Handbook (7th Edition):

Li, Qingbin. “Online sufficient dimensionality reduction for sequential high-dimensional time-series.” 2015. Web. 19 Mar 2019.

Vancouver:

Li Q. Online sufficient dimensionality reduction for sequential high-dimensional time-series. [Internet] [Masters thesis]. Georgia Tech; 2015. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/60385.

Council of Science Editors:

Li Q. Online sufficient dimensionality reduction for sequential high-dimensional time-series. [Masters Thesis]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/60385


Georgia Tech

2. Khalil, Elias B. Optimizing the Structure of Diffusion Networks: Theory and Algorithms.

Degree: MS, Computer Science, 2014, Georgia Tech

 How can we optimize the topology of a networked system to make it resilient to flus or malware, or also conducive to the spread of… (more)

Subjects/Keywords: networks; submodularity; supermodularity; optimization; diffusion of innovations; epidemics; graph theory; algorithms; social networks; information networks

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APA (6th Edition):

Khalil, E. B. (2014). Optimizing the Structure of Diffusion Networks: Theory and Algorithms. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55481

Chicago Manual of Style (16th Edition):

Khalil, Elias B. “Optimizing the Structure of Diffusion Networks: Theory and Algorithms.” 2014. Masters Thesis, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/55481.

MLA Handbook (7th Edition):

Khalil, Elias B. “Optimizing the Structure of Diffusion Networks: Theory and Algorithms.” 2014. Web. 19 Mar 2019.

Vancouver:

Khalil EB. Optimizing the Structure of Diffusion Networks: Theory and Algorithms. [Internet] [Masters thesis]. Georgia Tech; 2014. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/55481.

Council of Science Editors:

Khalil EB. Optimizing the Structure of Diffusion Networks: Theory and Algorithms. [Masters Thesis]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/55481


Georgia Tech

3. Patnaik, Kaushik. Adaptive learning in lasso models.

Degree: MS, Computer Science, 2015, Georgia Tech

 Regression with L1-regularization, Lasso, is a popular algorithm for recovering the sparsity pattern (also known as model selection) in linear models from observations contaminated by… (more)

Subjects/Keywords: Lasso; L1 regression; Adaptive methods; Active learning

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APA (6th Edition):

Patnaik, K. (2015). Adaptive learning in lasso models. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54353

Chicago Manual of Style (16th Edition):

Patnaik, Kaushik. “Adaptive learning in lasso models.” 2015. Masters Thesis, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/54353.

MLA Handbook (7th Edition):

Patnaik, Kaushik. “Adaptive learning in lasso models.” 2015. Web. 19 Mar 2019.

Vancouver:

Patnaik K. Adaptive learning in lasso models. [Internet] [Masters thesis]. Georgia Tech; 2015. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/54353.

Council of Science Editors:

Patnaik K. Adaptive learning in lasso models. [Masters Thesis]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/54353


Georgia Tech

4. Zhou, Yi. Stochastic algorithms for distributed optimization and machine learning.

Degree: PhD, Industrial and Systems Engineering, 2018, Georgia Tech

 In the big data era, machine learning acts as a powerful tool to help us make predictions and decisions. It has strong ties to the… (more)

Subjects/Keywords: Randomized algorithms; Stochastic optimization; Distributed optimization; Machine learning; Distributed machine learning; Finite-sum optimization

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APA (6th Edition):

Zhou, Y. (2018). Stochastic algorithms for distributed optimization and machine learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60256

Chicago Manual of Style (16th Edition):

Zhou, Yi. “Stochastic algorithms for distributed optimization and machine learning.” 2018. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/60256.

MLA Handbook (7th Edition):

Zhou, Yi. “Stochastic algorithms for distributed optimization and machine learning.” 2018. Web. 19 Mar 2019.

Vancouver:

Zhou Y. Stochastic algorithms for distributed optimization and machine learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/60256.

Council of Science Editors:

Zhou Y. Stochastic algorithms for distributed optimization and machine learning. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60256


Georgia Tech

5. Zhou, Ke. Extending low-rank matrix factorizations for emerging applications.

Degree: PhD, Computational Science and Engineering, 2013, Georgia Tech

 Low-rank matrix factorizations have become increasingly popular to project high dimensional data into latent spaces with small dimensions in order to obtain better understandings of… (more)

Subjects/Keywords: Matrix factorization; Collaborative filtering; Social network; Dimensional analysis Computer programs; Cluster analysis Data processing; Social networks

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APA (6th Edition):

Zhou, K. (2013). Extending low-rank matrix factorizations for emerging applications. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/50230

Chicago Manual of Style (16th Edition):

Zhou, Ke. “Extending low-rank matrix factorizations for emerging applications.” 2013. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/50230.

MLA Handbook (7th Edition):

Zhou, Ke. “Extending low-rank matrix factorizations for emerging applications.” 2013. Web. 19 Mar 2019.

Vancouver:

Zhou K. Extending low-rank matrix factorizations for emerging applications. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/50230.

Council of Science Editors:

Zhou K. Extending low-rank matrix factorizations for emerging applications. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/50230


Georgia Tech

6. Rangel Walteros, Pedro Andres. A non-asymptotic study of low-rank estimation of smooth kernels on graphs.

Degree: PhD, Mathematics, 2014, Georgia Tech

 This dissertation investigates the problem of estimating a kernel over a large graph based on a sample of noisy observations of linear measurements of the… (more)

Subjects/Keywords: Low-rank matrix completion; Kernels on graphs; High dimensional probability

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APA (6th Edition):

Rangel Walteros, P. A. (2014). A non-asymptotic study of low-rank estimation of smooth kernels on graphs. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/52988

Chicago Manual of Style (16th Edition):

Rangel Walteros, Pedro Andres. “A non-asymptotic study of low-rank estimation of smooth kernels on graphs.” 2014. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/52988.

MLA Handbook (7th Edition):

Rangel Walteros, Pedro Andres. “A non-asymptotic study of low-rank estimation of smooth kernels on graphs.” 2014. Web. 19 Mar 2019.

Vancouver:

Rangel Walteros PA. A non-asymptotic study of low-rank estimation of smooth kernels on graphs. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/52988.

Council of Science Editors:

Rangel Walteros PA. A non-asymptotic study of low-rank estimation of smooth kernels on graphs. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/52988


Georgia Tech

7. Lee, Joonseok. Local approaches for collaborative filtering.

Degree: PhD, Computer Science, 2015, Georgia Tech

 Recommendation systems are emerging as an important business application as the demand for personalized services in E-commerce increases. Collaborative filtering techniques are widely used for… (more)

Subjects/Keywords: Recommendation systems; Collaborative filtering; Machine learning; Local low-rank assumption; Matrix factorization; Matrix approximation; Ensemble collaborative ranking

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APA (6th Edition):

Lee, J. (2015). Local approaches for collaborative filtering. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53846

Chicago Manual of Style (16th Edition):

Lee, Joonseok. “Local approaches for collaborative filtering.” 2015. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/53846.

MLA Handbook (7th Edition):

Lee, Joonseok. “Local approaches for collaborative filtering.” 2015. Web. 19 Mar 2019.

Vancouver:

Lee J. Local approaches for collaborative filtering. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/53846.

Council of Science Editors:

Lee J. Local approaches for collaborative filtering. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53846


Georgia Tech

8. Berlind, Christopher. New insights on the power of active learning.

Degree: PhD, Computer Science, 2015, Georgia Tech

 Traditional supervised machine learning algorithms are expected to have access to a large corpus of labeled examples, but the massive amount of data available in… (more)

Subjects/Keywords: Machine learning; Learning theory; Active learning; Semi-supervised learning; Domain adaptation; Large margin learning

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APA (6th Edition):

Berlind, C. (2015). New insights on the power of active learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53948

Chicago Manual of Style (16th Edition):

Berlind, Christopher. “New insights on the power of active learning.” 2015. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/53948.

MLA Handbook (7th Edition):

Berlind, Christopher. “New insights on the power of active learning.” 2015. Web. 19 Mar 2019.

Vancouver:

Berlind C. New insights on the power of active learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/53948.

Council of Science Editors:

Berlind C. New insights on the power of active learning. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53948


Georgia Tech

9. Hagen, Matthew. Biological and clinical data integration and its applications in healthcare.

Degree: PhD, Computer Science, 2014, Georgia Tech

 Answers to the most complex biological questions are rarely determined solely from the experimental evidence. It requires subsequent analysis of many data sources that are… (more)

Subjects/Keywords: Biological database integration; Clinical data warehouse; Candidate gene prioritization; Disease; Diffusion kernel; Data mining; Ontology; Semantic similarity; Clustering; Intensive care unit; Hospital prioritization; Patient; Machine learning

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APA (6th Edition):

Hagen, M. (2014). Biological and clinical data integration and its applications in healthcare. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54267

Chicago Manual of Style (16th Edition):

Hagen, Matthew. “Biological and clinical data integration and its applications in healthcare.” 2014. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/54267.

MLA Handbook (7th Edition):

Hagen, Matthew. “Biological and clinical data integration and its applications in healthcare.” 2014. Web. 19 Mar 2019.

Vancouver:

Hagen M. Biological and clinical data integration and its applications in healthcare. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/54267.

Council of Science Editors:

Hagen M. Biological and clinical data integration and its applications in healthcare. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/54267


Georgia Tech

10. Du, Nan. Modeling, learning, and inference of high-dimensional asynchronous event data.

Degree: PhD, Computational Science and Engineering, 2016, Georgia Tech

 The increasing availability of temporal-spatial events produced from natural and social systems provides new opportunities and challenges for effective modeling the latent dynamics which inherently… (more)

Subjects/Keywords: Multivariate point process; Hawkes process; Survival analysis; Poisson process; Dirichlet process; Low-rank models; Social network analysis; Network structure inference; Information diffusion; Influence estimation; Influence maximization; Submodular maximization; Document clustering; Recommender systems

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APA (6th Edition):

Du, N. (2016). Modeling, learning, and inference of high-dimensional asynchronous event data. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55568

Chicago Manual of Style (16th Edition):

Du, Nan. “Modeling, learning, and inference of high-dimensional asynchronous event data.” 2016. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/55568.

MLA Handbook (7th Edition):

Du, Nan. “Modeling, learning, and inference of high-dimensional asynchronous event data.” 2016. Web. 19 Mar 2019.

Vancouver:

Du N. Modeling, learning, and inference of high-dimensional asynchronous event data. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/55568.

Council of Science Editors:

Du N. Modeling, learning, and inference of high-dimensional asynchronous event data. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/55568


Georgia Tech

11. Xia, Dong. Statistical inference for large matrices.

Degree: PhD, Mathematics, 2016, Georgia Tech

 This thesis covers two topics on matrix analysis and estimation in machine learning and statistics. The first topic is about density matrix estimation with application… (more)

Subjects/Keywords: Low rank; Matrix estimation; Singular vectors; Random perturbation

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APA (6th Edition):

Xia, D. (2016). Statistical inference for large matrices. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55632

Chicago Manual of Style (16th Edition):

Xia, Dong. “Statistical inference for large matrices.” 2016. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/55632.

MLA Handbook (7th Edition):

Xia, Dong. “Statistical inference for large matrices.” 2016. Web. 19 Mar 2019.

Vancouver:

Xia D. Statistical inference for large matrices. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/55632.

Council of Science Editors:

Xia D. Statistical inference for large matrices. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/55632


Georgia Tech

12. Turner, David M. Construction of representative 3D microstructures from complete and partial statistics.

Degree: PhD, Mechanical Engineering, 2016, Georgia Tech

 The principle concern of the material scientist is the connection between microstructure, properties, and processing. Microstructure is characterized via experimental measurements of geometry at the… (more)

Subjects/Keywords: Microstructure; 2-point statistics; Reconstruction; Solid Texture Synthesis

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APA (6th Edition):

Turner, D. M. (2016). Construction of representative 3D microstructures from complete and partial statistics. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/56337

Chicago Manual of Style (16th Edition):

Turner, David M. “Construction of representative 3D microstructures from complete and partial statistics.” 2016. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/56337.

MLA Handbook (7th Edition):

Turner, David M. “Construction of representative 3D microstructures from complete and partial statistics.” 2016. Web. 19 Mar 2019.

Vancouver:

Turner DM. Construction of representative 3D microstructures from complete and partial statistics. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/56337.

Council of Science Editors:

Turner DM. Construction of representative 3D microstructures from complete and partial statistics. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/56337


Georgia Tech

13. Pan, Yunpeng. Learning control via probabilistic trajectory optimization.

Degree: PhD, Aerospace Engineering, 2017, Georgia Tech

 A central problem in the field of robotics is to develop real-time planning and control algorithms for autonomous systems to behave intelligently under uncertainty. While… (more)

Subjects/Keywords: Optimal control; Robotics; Artificial intelligence; Machine learning

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APA (6th Edition):

Pan, Y. (2017). Learning control via probabilistic trajectory optimization. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59278

Chicago Manual of Style (16th Edition):

Pan, Yunpeng. “Learning control via probabilistic trajectory optimization.” 2017. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/59278.

MLA Handbook (7th Edition):

Pan, Yunpeng. “Learning control via probabilistic trajectory optimization.” 2017. Web. 19 Mar 2019.

Vancouver:

Pan Y. Learning control via probabilistic trajectory optimization. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/59278.

Council of Science Editors:

Pan Y. Learning control via probabilistic trajectory optimization. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/59278


Georgia Tech

14. Casas, Maria Elena. Analysis of lipid storage in C. elegans enabled by image processing and microfluidics.

Degree: PhD, Chemical and Biomolecular Engineering, 2017, Georgia Tech

 While the genetic origin of obesity has yet to be fully understood, the risk of this serious health issue has been linked to fat and… (more)

Subjects/Keywords: C. elegans; Microfluidics; Lipid droplets; Image processing; Granulometry; Metabolism; Diet; Genetic screen; Atlastin

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APA (6th Edition):

Casas, M. E. (2017). Analysis of lipid storage in C. elegans enabled by image processing and microfluidics. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59803

Chicago Manual of Style (16th Edition):

Casas, Maria Elena. “Analysis of lipid storage in C. elegans enabled by image processing and microfluidics.” 2017. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/59803.

MLA Handbook (7th Edition):

Casas, Maria Elena. “Analysis of lipid storage in C. elegans enabled by image processing and microfluidics.” 2017. Web. 19 Mar 2019.

Vancouver:

Casas ME. Analysis of lipid storage in C. elegans enabled by image processing and microfluidics. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/59803.

Council of Science Editors:

Casas ME. Analysis of lipid storage in C. elegans enabled by image processing and microfluidics. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/59803


Georgia Tech

15. Farajtabar, Mehrdad. Point process modeling and optimization of social networks.

Degree: PhD, Computational Science and Engineering, 2018, Georgia Tech

 Online social media such as Facebook and Twitter and communities such as Wikipedia and Stackoverflow turn to become an inseparable part of today's lifestyle. Users… (more)

Subjects/Keywords: Point processes

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APA (6th Edition):

Farajtabar, M. (2018). Point process modeling and optimization of social networks. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59858

Chicago Manual of Style (16th Edition):

Farajtabar, Mehrdad. “Point process modeling and optimization of social networks.” 2018. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/59858.

MLA Handbook (7th Edition):

Farajtabar, Mehrdad. “Point process modeling and optimization of social networks.” 2018. Web. 19 Mar 2019.

Vancouver:

Farajtabar M. Point process modeling and optimization of social networks. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/59858.

Council of Science Editors:

Farajtabar M. Point process modeling and optimization of social networks. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/59858


Georgia Tech

16. Wang, Yichen. Modeling, predicting, and guiding users' temporal behaviors.

Degree: PhD, Mathematics, 2018, Georgia Tech

 The increasing availability and granularity of temporal event data produced from user activities in online media, social networks and health informatics provide new opportunities and… (more)

Subjects/Keywords: Point processes; Hawkes processes; Survival analysis; Low-rank models; Mass transport; Fokker Planck equation; Stochastic optimal control; Reinforcement learning; Social network analysis; Information diffusion; Recommendation systems

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APA (6th Edition):

Wang, Y. (2018). Modeling, predicting, and guiding users' temporal behaviors. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60208

Chicago Manual of Style (16th Edition):

Wang, Yichen. “Modeling, predicting, and guiding users' temporal behaviors.” 2018. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/60208.

MLA Handbook (7th Edition):

Wang, Yichen. “Modeling, predicting, and guiding users' temporal behaviors.” 2018. Web. 19 Mar 2019.

Vancouver:

Wang Y. Modeling, predicting, and guiding users' temporal behaviors. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/60208.

Council of Science Editors:

Wang Y. Modeling, predicting, and guiding users' temporal behaviors. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60208


Georgia Tech

17. Moore, Michael George. Maximum likelihood estimation of Poisson and Hawkes processes and extensions to Hawkes process analysis.

Degree: PhD, Electrical and Computer Engineering, 2018, Georgia Tech

 The purpose of this work is to improve our ability to extract information from data generated by Poisson and Hawkes processes. Our principal focus is… (more)

Subjects/Keywords: Poisson; Hawkes; Point process; Estimation

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APA (6th Edition):

Moore, M. G. (2018). Maximum likelihood estimation of Poisson and Hawkes processes and extensions to Hawkes process analysis. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60727

Chicago Manual of Style (16th Edition):

Moore, Michael George. “Maximum likelihood estimation of Poisson and Hawkes processes and extensions to Hawkes process analysis.” 2018. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/60727.

MLA Handbook (7th Edition):

Moore, Michael George. “Maximum likelihood estimation of Poisson and Hawkes processes and extensions to Hawkes process analysis.” 2018. Web. 19 Mar 2019.

Vancouver:

Moore MG. Maximum likelihood estimation of Poisson and Hawkes processes and extensions to Hawkes process analysis. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/60727.

Council of Science Editors:

Moore MG. Maximum likelihood estimation of Poisson and Hawkes processes and extensions to Hawkes process analysis. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60727


Georgia Tech

18. Liang, Yingyu. Modern aspects of unsupervised learning.

Degree: PhD, Computer Science, 2014, Georgia Tech

 Unsupervised learning has become more and more important due to the recent explosion of data. Clustering, a key topic in unsupervised learning, is a well-studied… (more)

Subjects/Keywords: Unsupervised learning; Clustering; Perturbation resilience; Distributed clustering; Community detection

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APA (6th Edition):

Liang, Y. (2014). Modern aspects of unsupervised learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/52282

Chicago Manual of Style (16th Edition):

Liang, Yingyu. “Modern aspects of unsupervised learning.” 2014. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/52282.

MLA Handbook (7th Edition):

Liang, Yingyu. “Modern aspects of unsupervised learning.” 2014. Web. 19 Mar 2019.

Vancouver:

Liang Y. Modern aspects of unsupervised learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/52282.

Council of Science Editors:

Liang Y. Modern aspects of unsupervised learning. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/52282

19. Xie, Bo. Algorithms and analysis for non-convex optimization problems in machine learning.

Degree: PhD, Computational Science and Engineering, 2017, Georgia Tech

 In this thesis, we propose efficient algorithms and provide theoretical analysis through the angle of spectral methods for some important non-convex optimization problems in machine… (more)

Subjects/Keywords: Machine learning; Non-convex optimization; Spectral algorithms; Neural networks; Deep learning

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APA (6th Edition):

Xie, B. (2017). Algorithms and analysis for non-convex optimization problems in machine learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58642

Chicago Manual of Style (16th Edition):

Xie, Bo. “Algorithms and analysis for non-convex optimization problems in machine learning.” 2017. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/58642.

MLA Handbook (7th Edition):

Xie, Bo. “Algorithms and analysis for non-convex optimization problems in machine learning.” 2017. Web. 19 Mar 2019.

Vancouver:

Xie B. Algorithms and analysis for non-convex optimization problems in machine learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/58642.

Council of Science Editors:

Xie B. Algorithms and analysis for non-convex optimization problems in machine learning. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58642

20. Li, Liangda. Influence modeling in behavioral data.

Degree: PhD, Computational Science and Engineering, 2015, Georgia Tech

 Understanding influence in behavioral data has become increasingly important in analyzing the cause and effect of human behaviors under various scenarios. Influence modeling enables us… (more)

Subjects/Keywords: Influence modeling; Behavioral data;

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APA (6th Edition):

Li, L. (2015). Influence modeling in behavioral data. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53879

Chicago Manual of Style (16th Edition):

Li, Liangda. “Influence modeling in behavioral data.” 2015. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/53879.

MLA Handbook (7th Edition):

Li, Liangda. “Influence modeling in behavioral data.” 2015. Web. 19 Mar 2019.

Vancouver:

Li L. Influence modeling in behavioral data. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/53879.

Council of Science Editors:

Li L. Influence modeling in behavioral data. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53879

21. Burns, Paul D. Gene finding in eukaryotic genomes using external information and machine learning techniques.

Degree: PhD, Biomedical Engineering (Joint GT/Emory Department), 2013, Georgia Tech

 Gene finding in eukaryotic genomes is an essential part of a comprehensive approach to modern systems biology. Most methods developed in the past rely on… (more)

Subjects/Keywords: Gene finding; RNA-seq; Machine learning; SVM; Genomics; Eukaryotic cells; Gene mapping; Machine learning; Nucleotide sequence

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APA (6th Edition):

Burns, P. D. (2013). Gene finding in eukaryotic genomes using external information and machine learning techniques. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/49023

Chicago Manual of Style (16th Edition):

Burns, Paul D. “Gene finding in eukaryotic genomes using external information and machine learning techniques.” 2013. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/49023.

MLA Handbook (7th Edition):

Burns, Paul D. “Gene finding in eukaryotic genomes using external information and machine learning techniques.” 2013. Web. 19 Mar 2019.

Vancouver:

Burns PD. Gene finding in eukaryotic genomes using external information and machine learning techniques. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/49023.

Council of Science Editors:

Burns PD. Gene finding in eukaryotic genomes using external information and machine learning techniques. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/49023

22. Balasubramanian, Krishnakumar. Learning matrix and functional models in high-dimensions.

Degree: PhD, Computer Science, 2014, Georgia Tech

 Statistical machine learning methods provide us with a principled framework for extracting meaningful information from noisy high-dimensional data sets. A significant feature of such procedures… (more)

Subjects/Keywords: Statistics; Machine learning; Matrix; Kernel; Consistency

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APA (6th Edition):

Balasubramanian, K. (2014). Learning matrix and functional models in high-dimensions. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/52284

Chicago Manual of Style (16th Edition):

Balasubramanian, Krishnakumar. “Learning matrix and functional models in high-dimensions.” 2014. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/52284.

MLA Handbook (7th Edition):

Balasubramanian, Krishnakumar. “Learning matrix and functional models in high-dimensions.” 2014. Web. 19 Mar 2019.

Vancouver:

Balasubramanian K. Learning matrix and functional models in high-dimensions. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/52284.

Council of Science Editors:

Balasubramanian K. Learning matrix and functional models in high-dimensions. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/52284

23. Tzou, Nicholas. Low-cost sub-Nyquist sampling hardware and algorithm co-design for wideband and high-speed signal characterization and measurement.

Degree: PhD, Electrical and Computer Engineering, 2014, Georgia Tech

 Cost reduction has been and will continue to be a primary driving force in the evolution of hardware design and associated technologies. The objective of… (more)

Subjects/Keywords: Low-cost; Sub-Nyquist; Algorithm; Hardware; Measurement; Multi-rate; Band-interleaved; Undersampling; Jitter; Crosstalk separation; Broadband communication systems Equipment and supplies; Algorithms

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APA (6th Edition):

Tzou, N. (2014). Low-cost sub-Nyquist sampling hardware and algorithm co-design for wideband and high-speed signal characterization and measurement. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/51876

Chicago Manual of Style (16th Edition):

Tzou, Nicholas. “Low-cost sub-Nyquist sampling hardware and algorithm co-design for wideband and high-speed signal characterization and measurement.” 2014. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/51876.

MLA Handbook (7th Edition):

Tzou, Nicholas. “Low-cost sub-Nyquist sampling hardware and algorithm co-design for wideband and high-speed signal characterization and measurement.” 2014. Web. 19 Mar 2019.

Vancouver:

Tzou N. Low-cost sub-Nyquist sampling hardware and algorithm co-design for wideband and high-speed signal characterization and measurement. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/51876.

Council of Science Editors:

Tzou N. Low-cost sub-Nyquist sampling hardware and algorithm co-design for wideband and high-speed signal characterization and measurement. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/51876

24. Ganti Mahapatruni, Ravi Sastry. New formulations for active learning.

Degree: PhD, Computer Science, 2014, Georgia Tech

 In this thesis, we provide computationally efficient algorithms with provable statistical guarantees, for the problem of active learning, by using ideas from sequential analysis. We… (more)

Subjects/Keywords: Active learning; Sequential analysis; Stochastic optimization; Active learning; Algorithms; Sequential analysis; Mathematical optimization; Machine learning

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APA (6th Edition):

Ganti Mahapatruni, R. S. (2014). New formulations for active learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/51801

Chicago Manual of Style (16th Edition):

Ganti Mahapatruni, Ravi Sastry. “New formulations for active learning.” 2014. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/51801.

MLA Handbook (7th Edition):

Ganti Mahapatruni, Ravi Sastry. “New formulations for active learning.” 2014. Web. 19 Mar 2019.

Vancouver:

Ganti Mahapatruni RS. New formulations for active learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/51801.

Council of Science Editors:

Ganti Mahapatruni RS. New formulations for active learning. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/51801

25. Yang, Shuang-Hong. Predictive models for online human activities.

Degree: PhD, Computing, 2012, Georgia Tech

 The availability and scale of user generated data in online systems raises tremendous challenges and opportunities to analytic study of human activities. Effective modeling of… (more)

Subjects/Keywords: Social contagion; Collaborative competitive filtering; Social ties; Behavior prediction; User-generated data; Redictive models; Online human activities; Language gap; User cognitive aspects; Content mining; Behavior-relation interplay; User-generated content; User interfaces (Computer systems); Data mining

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Yang, S. (2012). Predictive models for online human activities. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/43689

Chicago Manual of Style (16th Edition):

Yang, Shuang-Hong. “Predictive models for online human activities.” 2012. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/43689.

MLA Handbook (7th Edition):

Yang, Shuang-Hong. “Predictive models for online human activities.” 2012. Web. 19 Mar 2019.

Vancouver:

Yang S. Predictive models for online human activities. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/43689.

Council of Science Editors:

Yang S. Predictive models for online human activities. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/43689

26. Lee, Joo Hwan. Relaxing coherence for modern learning applications.

Degree: PhD, Computer Science, 2017, Georgia Tech

 The main objective of this research is to efficiently execute learning (model training) of modern machine learning (ML) applications. The recent explosion in data has… (more)

Subjects/Keywords: Relaxing coherence; Stale value tolerance; Machine learning acceleration; Parallel learning; Computer architecture

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APA (6th Edition):

Lee, J. H. (2017). Relaxing coherence for modern learning applications. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58203

Chicago Manual of Style (16th Edition):

Lee, Joo Hwan. “Relaxing coherence for modern learning applications.” 2017. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/58203.

MLA Handbook (7th Edition):

Lee, Joo Hwan. “Relaxing coherence for modern learning applications.” 2017. Web. 19 Mar 2019.

Vancouver:

Lee JH. Relaxing coherence for modern learning applications. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/58203.

Council of Science Editors:

Lee JH. Relaxing coherence for modern learning applications. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58203

27. Xu, Hongteng. Point process-based modeling and analysis of asynchronous event sequences.

Degree: PhD, Electrical and Computer Engineering, 2017, Georgia Tech

 Real-world interactions among multiple entities, such as user behaviors in social networks, job hunting and hopping, and diseases and their complications, often exhibit self-triggering and… (more)

Subjects/Keywords: Point process; Hawkes process; correcting process; Granger causality; impact function; infectivity network; multi-task learning; Dirichlet mixture model; structural regularizer; disciriminative learning; doubly-censored data; attractiveness model

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APA (6th Edition):

Xu, H. (2017). Point process-based modeling and analysis of asynchronous event sequences. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58690

Chicago Manual of Style (16th Edition):

Xu, Hongteng. “Point process-based modeling and analysis of asynchronous event sequences.” 2017. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/58690.

MLA Handbook (7th Edition):

Xu, Hongteng. “Point process-based modeling and analysis of asynchronous event sequences.” 2017. Web. 19 Mar 2019.

Vancouver:

Xu H. Point process-based modeling and analysis of asynchronous event sequences. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/58690.

Council of Science Editors:

Xu H. Point process-based modeling and analysis of asynchronous event sequences. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58690

28. Cecen, Ahmet. Calculation, utilization, and inference of spatial statistics in practical spatio-temporal data.

Degree: PhD, Computational Science and Engineering, 2017, Georgia Tech

 The direct influence of spatial and structural arrangement in various length scales to the performance characteristics of materials is a core premise of materials science.… (more)

Subjects/Keywords: Materials; Informatics; Data science; Image processing; Spatial statistics; Texture synthesis; Microstructure generator

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APA (6th Edition):

Cecen, A. (2017). Calculation, utilization, and inference of spatial statistics in practical spatio-temporal data. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58723

Chicago Manual of Style (16th Edition):

Cecen, Ahmet. “Calculation, utilization, and inference of spatial statistics in practical spatio-temporal data.” 2017. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/58723.

MLA Handbook (7th Edition):

Cecen, Ahmet. “Calculation, utilization, and inference of spatial statistics in practical spatio-temporal data.” 2017. Web. 19 Mar 2019.

Vancouver:

Cecen A. Calculation, utilization, and inference of spatial statistics in practical spatio-temporal data. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/58723.

Council of Science Editors:

Cecen A. Calculation, utilization, and inference of spatial statistics in practical spatio-temporal data. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58723

29. Arslan, Oktay. Machine learning and dynamic programming algorithms for motion planning and control.

Degree: PhD, Aerospace Engineering, 2015, Georgia Tech

 Robot motion planning is one of the central problems in robotics, and has received considerable amount of attention not only from roboticists but also from… (more)

Subjects/Keywords: Robotic motion planning; Sampling-based algorithms; Rapidly-exploring random trees; Dynamic programming; Machine learning; Closed-loop prediction; High-level route planning

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APA (6th Edition):

Arslan, O. (2015). Machine learning and dynamic programming algorithms for motion planning and control. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54317

Chicago Manual of Style (16th Edition):

Arslan, Oktay. “Machine learning and dynamic programming algorithms for motion planning and control.” 2015. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/54317.

MLA Handbook (7th Edition):

Arslan, Oktay. “Machine learning and dynamic programming algorithms for motion planning and control.” 2015. Web. 19 Mar 2019.

Vancouver:

Arslan O. Machine learning and dynamic programming algorithms for motion planning and control. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/54317.

Council of Science Editors:

Arslan O. Machine learning and dynamic programming algorithms for motion planning and control. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/54317

30. Tran, Long Quoc. Efficient inference algorithms for network activities.

Degree: PhD, Computational Science and Engineering, 2015, Georgia Tech

 The real social network and associated communities are often hidden under the declared friend or group lists in social networks. We usually observe the manifestation… (more)

Subjects/Keywords: Hawkes; Inference

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APA (6th Edition):

Tran, L. Q. (2015). Efficient inference algorithms for network activities. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53499

Chicago Manual of Style (16th Edition):

Tran, Long Quoc. “Efficient inference algorithms for network activities.” 2015. Doctoral Dissertation, Georgia Tech. Accessed March 19, 2019. http://hdl.handle.net/1853/53499.

MLA Handbook (7th Edition):

Tran, Long Quoc. “Efficient inference algorithms for network activities.” 2015. Web. 19 Mar 2019.

Vancouver:

Tran LQ. Efficient inference algorithms for network activities. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1853/53499.

Council of Science Editors:

Tran LQ. Efficient inference algorithms for network activities. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53499

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