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You searched for +publisher:"Georgia Tech" +contributor:("Song, Le"). Showing records 1 – 30 of 41 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 September 27, 2020. http://hdl.handle.net/1853/60385.

MLA Handbook (7th Edition):

Li, Qingbin. “Online sufficient dimensionality reduction for sequential high-dimensional time-series.” 2015. Web. 27 Sep 2020.

Vancouver:

Li Q. Online sufficient dimensionality reduction for sequential high-dimensional time-series. [Internet] [Masters thesis]. Georgia Tech; 2015. [cited 2020 Sep 27]. 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. Dai, Hanjun. Learning neural algorithms with graph structures.

Degree: PhD, Computational Science and Engineering, 2020, Georgia Tech

 Graph structures, like syntax trees, social networks, and programs, are ubiquitous in many real world applications including knowledge graph inference, chemistry and social network analysis.… (more)

Subjects/Keywords: Deep learning; Graph representation learning; Structured generative modeling; Structured prediction; Reinforcement learning; Chemistry; Bioinformatics; Program understanding

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APA (6th Edition):

Dai, H. (2020). Learning neural algorithms with graph structures. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62737

Chicago Manual of Style (16th Edition):

Dai, Hanjun. “Learning neural algorithms with graph structures.” 2020. Doctoral Dissertation, Georgia Tech. Accessed September 27, 2020. http://hdl.handle.net/1853/62737.

MLA Handbook (7th Edition):

Dai, Hanjun. “Learning neural algorithms with graph structures.” 2020. Web. 27 Sep 2020.

Vancouver:

Dai H. Learning neural algorithms with graph structures. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/1853/62737.

Council of Science Editors:

Dai H. Learning neural algorithms with graph structures. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/62737


Georgia Tech

3. 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 September 27, 2020. http://hdl.handle.net/1853/60256.

MLA Handbook (7th Edition):

Zhou, Yi. “Stochastic algorithms for distributed optimization and machine learning.” 2018. Web. 27 Sep 2020.

Vancouver:

Zhou Y. Stochastic algorithms for distributed optimization and machine learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2020 Sep 27]. 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

4. 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 September 27, 2020. http://hdl.handle.net/1853/55481.

MLA Handbook (7th Edition):

Khalil, Elias B. “Optimizing the Structure of Diffusion Networks: Theory and Algorithms.” 2014. Web. 27 Sep 2020.

Vancouver:

Khalil EB. Optimizing the Structure of Diffusion Networks: Theory and Algorithms. [Internet] [Masters thesis]. Georgia Tech; 2014. [cited 2020 Sep 27]. 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

5. Khalil, Elias. Towards tighter integration of machine learning and discrete optimization.

Degree: PhD, Computational Science and Engineering, 2019, Georgia Tech

 Discrete Optimization algorithms underlie intelligent decision-making in a wide variety of domains. From airline fleet scheduling to data center resource management and matching in ride-sharing… (more)

Subjects/Keywords: Discrete optimization; Integer programming; Machine learning; Deep learning; Adversarial machine learning; Reinforcement learning

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APA (6th Edition):

Khalil, E. (2019). Towards tighter integration of machine learning and discrete optimization. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62668

Chicago Manual of Style (16th Edition):

Khalil, Elias. “Towards tighter integration of machine learning and discrete optimization.” 2019. Doctoral Dissertation, Georgia Tech. Accessed September 27, 2020. http://hdl.handle.net/1853/62668.

MLA Handbook (7th Edition):

Khalil, Elias. “Towards tighter integration of machine learning and discrete optimization.” 2019. Web. 27 Sep 2020.

Vancouver:

Khalil E. Towards tighter integration of machine learning and discrete optimization. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/1853/62668.

Council of Science Editors:

Khalil E. Towards tighter integration of machine learning and discrete optimization. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/62668


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 September 27, 2020. 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. 27 Sep 2020.

Vancouver:

Rangel Walteros PA. A non-asymptotic study of low-rank estimation of smooth kernels on graphs. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2020 Sep 27]. 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. 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 September 27, 2020. http://hdl.handle.net/1853/50230.

MLA Handbook (7th Edition):

Zhou, Ke. “Extending low-rank matrix factorizations for emerging applications.” 2013. Web. 27 Sep 2020.

Vancouver:

Zhou K. Extending low-rank matrix factorizations for emerging applications. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2020 Sep 27]. 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

8. 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 September 27, 2020. http://hdl.handle.net/1853/59858.

MLA Handbook (7th Edition):

Farajtabar, Mehrdad. “Point process modeling and optimization of social networks.” 2018. Web. 27 Sep 2020.

Vancouver:

Farajtabar M. Point process modeling and optimization of social networks. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2020 Sep 27]. 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

9. 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 September 27, 2020. http://hdl.handle.net/1853/53846.

MLA Handbook (7th Edition):

Lee, Joonseok. “Local approaches for collaborative filtering.” 2015. Web. 27 Sep 2020.

Vancouver:

Lee J. Local approaches for collaborative filtering. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2020 Sep 27]. 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

10. 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 September 27, 2020. http://hdl.handle.net/1853/53948.

MLA Handbook (7th Edition):

Berlind, Christopher. “New insights on the power of active learning.” 2015. Web. 27 Sep 2020.

Vancouver:

Berlind C. New insights on the power of active learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2020 Sep 27]. 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

11. 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 September 27, 2020. http://hdl.handle.net/1853/54267.

MLA Handbook (7th Edition):

Hagen, Matthew. “Biological and clinical data integration and its applications in healthcare.” 2014. Web. 27 Sep 2020.

Vancouver:

Hagen M. Biological and clinical data integration and its applications in healthcare. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2020 Sep 27]. 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

12. 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 September 27, 2020. http://hdl.handle.net/1853/54353.

MLA Handbook (7th Edition):

Patnaik, Kaushik. “Adaptive learning in lasso models.” 2015. Web. 27 Sep 2020.

Vancouver:

Patnaik K. Adaptive learning in lasso models. [Internet] [Masters thesis]. Georgia Tech; 2015. [cited 2020 Sep 27]. 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

13. 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 September 27, 2020. http://hdl.handle.net/1853/55568.

MLA Handbook (7th Edition):

Du, Nan. “Modeling, learning, and inference of high-dimensional asynchronous event data.” 2016. Web. 27 Sep 2020.

Vancouver:

Du N. Modeling, learning, and inference of high-dimensional asynchronous event data. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2020 Sep 27]. 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

14. 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 September 27, 2020. http://hdl.handle.net/1853/55632.

MLA Handbook (7th Edition):

Xia, Dong. “Statistical inference for large matrices.” 2016. Web. 27 Sep 2020.

Vancouver:

Xia D. Statistical inference for large matrices. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2020 Sep 27]. 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

15. 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 September 27, 2020. 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. 27 Sep 2020.

Vancouver:

Turner DM. Construction of representative 3D microstructures from complete and partial statistics. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2020 Sep 27]. 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

16. Zhou, Zhiqiang. Theory and applications of first-order methods for convex optimization with function constraints.

Degree: PhD, Industrial and Systems Engineering, 2020, Georgia Tech

 This dissertation focuses on the development of efficient first-order methods for function constrained convex optimization and their applications in a few different areas, including healthcare,… (more)

Subjects/Keywords: First-order methods; Function constrained optimization; Machine learning

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APA (6th Edition):

Zhou, Z. (2020). Theory and applications of first-order methods for convex optimization with function constraints. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/63664

Chicago Manual of Style (16th Edition):

Zhou, Zhiqiang. “Theory and applications of first-order methods for convex optimization with function constraints.” 2020. Doctoral Dissertation, Georgia Tech. Accessed September 27, 2020. http://hdl.handle.net/1853/63664.

MLA Handbook (7th Edition):

Zhou, Zhiqiang. “Theory and applications of first-order methods for convex optimization with function constraints.” 2020. Web. 27 Sep 2020.

Vancouver:

Zhou Z. Theory and applications of first-order methods for convex optimization with function constraints. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/1853/63664.

Council of Science Editors:

Zhou Z. Theory and applications of first-order methods for convex optimization with function constraints. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/63664


Georgia Tech

17. Liu, Weiyang. Deep representation learning on hypersphere.

Degree: PhD, Computational Science and Engineering, 2020, Georgia Tech

 How to efficiently learn discriminative deep features is arguably one of the core problems in deep learning, since it can benefit a lot of downstream… (more)

Subjects/Keywords: Machine learning; Visual recognition; Deep learning; Representation learning; Neural networks; Hypersphere

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APA (6th Edition):

Liu, W. (2020). Deep representation learning on hypersphere. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/63670

Chicago Manual of Style (16th Edition):

Liu, Weiyang. “Deep representation learning on hypersphere.” 2020. Doctoral Dissertation, Georgia Tech. Accessed September 27, 2020. http://hdl.handle.net/1853/63670.

MLA Handbook (7th Edition):

Liu, Weiyang. “Deep representation learning on hypersphere.” 2020. Web. 27 Sep 2020.

Vancouver:

Liu W. Deep representation learning on hypersphere. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/1853/63670.

Council of Science Editors:

Liu W. Deep representation learning on hypersphere. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/63670


Georgia Tech

18. Chen, Shang-Tse. AI-infused security: Robust defense by bridging theory and practice.

Degree: PhD, Computational Science and Engineering, 2019, Georgia Tech

 While Artificial Intelligence (AI) has tremendous potential as a defense against real-world cybersecurity threats, understanding the capabilities and robustness of AI remains a fundamental challenge.… (more)

Subjects/Keywords: Security; Cybersecurity; Machine learning; Artificial Intelligence; Adversarial machine learning; Game theory; Boosting; Fire risk

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APA (6th Edition):

Chen, S. (2019). AI-infused security: Robust defense by bridging theory and practice. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62296

Chicago Manual of Style (16th Edition):

Chen, Shang-Tse. “AI-infused security: Robust defense by bridging theory and practice.” 2019. Doctoral Dissertation, Georgia Tech. Accessed September 27, 2020. http://hdl.handle.net/1853/62296.

MLA Handbook (7th Edition):

Chen, Shang-Tse. “AI-infused security: Robust defense by bridging theory and practice.” 2019. Web. 27 Sep 2020.

Vancouver:

Chen S. AI-infused security: Robust defense by bridging theory and practice. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/1853/62296.

Council of Science Editors:

Chen S. AI-infused security: Robust defense by bridging theory and practice. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/62296


Georgia Tech

19. Ramamurthy, Arun. A reinforcement learning framework for the automation of engineering decisions in complex systems.

Degree: PhD, Aerospace Engineering, 2019, Georgia Tech

 The process of engineering design is characterized by a series of decisions that determine the performance of the final product. Engineers are faced with decisions,… (more)

Subjects/Keywords: Reinforcement learning; Artificial intelligence; Engineering design; Imitation learning; CAD2Vec

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APA (6th Edition):

Ramamurthy, A. (2019). A reinforcement learning framework for the automation of engineering decisions in complex systems. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62626

Chicago Manual of Style (16th Edition):

Ramamurthy, Arun. “A reinforcement learning framework for the automation of engineering decisions in complex systems.” 2019. Doctoral Dissertation, Georgia Tech. Accessed September 27, 2020. http://hdl.handle.net/1853/62626.

MLA Handbook (7th Edition):

Ramamurthy, Arun. “A reinforcement learning framework for the automation of engineering decisions in complex systems.” 2019. Web. 27 Sep 2020.

Vancouver:

Ramamurthy A. A reinforcement learning framework for the automation of engineering decisions in complex systems. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/1853/62626.

Council of Science Editors:

Ramamurthy A. A reinforcement learning framework for the automation of engineering decisions in complex systems. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/62626


Georgia Tech

20. 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 September 27, 2020. http://hdl.handle.net/1853/52282.

MLA Handbook (7th Edition):

Liang, Yingyu. “Modern aspects of unsupervised learning.” 2014. Web. 27 Sep 2020.

Vancouver:

Liang Y. Modern aspects of unsupervised learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2020 Sep 27]. 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

21. 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 September 27, 2020. http://hdl.handle.net/1853/52284.

MLA Handbook (7th Edition):

Balasubramanian, Krishnakumar. “Learning matrix and functional models in high-dimensions.” 2014. Web. 27 Sep 2020.

Vancouver:

Balasubramanian K. Learning matrix and functional models in high-dimensions. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2020 Sep 27]. 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

22. 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 September 27, 2020. http://hdl.handle.net/1853/51801.

MLA Handbook (7th Edition):

Ganti Mahapatruni, Ravi Sastry. “New formulations for active learning.” 2014. Web. 27 Sep 2020.

Vancouver:

Ganti Mahapatruni RS. New formulations for active learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2020 Sep 27]. 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

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 September 27, 2020. 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. 27 Sep 2020.

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 2020 Sep 27]. 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. 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 (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 September 27, 2020. http://hdl.handle.net/1853/43689.

MLA Handbook (7th Edition):

Yang, Shuang-Hong. “Predictive models for online human activities.” 2012. Web. 27 Sep 2020.

Vancouver:

Yang S. Predictive models for online human activities. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2020 Sep 27]. 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

25. 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 September 27, 2020. 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. 27 Sep 2020.

Vancouver:

Burns PD. Gene finding in eukaryotic genomes using external information and machine learning techniques. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2020 Sep 27]. 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

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 September 27, 2020. http://hdl.handle.net/1853/58203.

MLA Handbook (7th Edition):

Lee, Joo Hwan. “Relaxing coherence for modern learning applications.” 2017. Web. 27 Sep 2020.

Vancouver:

Lee JH. Relaxing coherence for modern learning applications. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2020 Sep 27]. 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. 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 September 27, 2020. http://hdl.handle.net/1853/60208.

MLA Handbook (7th Edition):

Wang, Yichen. “Modeling, predicting, and guiding users' temporal behaviors.” 2018. Web. 27 Sep 2020.

Vancouver:

Wang Y. Modeling, predicting, and guiding users' temporal behaviors. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2020 Sep 27]. 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

28. 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 September 27, 2020. 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. 27 Sep 2020.

Vancouver:

Casas ME. Analysis of lipid storage in C. elegans enabled by image processing and microfluidics. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2020 Sep 27]. 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

29. 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 September 27, 2020. 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. 27 Sep 2020.

Vancouver:

Moore MG. Maximum likelihood estimation of Poisson and Hawkes processes and extensions to Hawkes process analysis. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2020 Sep 27]. 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

30. 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 September 27, 2020. 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. 27 Sep 2020.

Vancouver:

Xie B. Algorithms and analysis for non-convex optimization problems in machine learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2020 Sep 27]. 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

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