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You searched for +publisher:"University of Texas – Austin" +contributor:("Caramanis, Constantine"). Showing records 1 – 30 of 48 total matches.

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University of Texas – Austin

1. -4028-5309. Finding good enough coins under symmetric and asymmetric information.

Degree: Electrical and Computer Engineering, 2017, University of Texas – Austin

 We study the problem of returning m coins with biases above 0:5. These good enough coins that are returned by the agent should be acceptable… (more)

Subjects/Keywords: Multi-armed bandits; Sequential hypothesis testing; FWER control; Information asymmetry

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APA (6th Edition):

-4028-5309. (2017). Finding good enough coins under symmetric and asymmetric information. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68220

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

-4028-5309. “Finding good enough coins under symmetric and asymmetric information.” 2017. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/68220.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

-4028-5309. “Finding good enough coins under symmetric and asymmetric information.” 2017. Web. 23 Apr 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-4028-5309. Finding good enough coins under symmetric and asymmetric information. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/68220.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-4028-5309. Finding good enough coins under symmetric and asymmetric information. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/68220

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

2. -6463-6575. Numeric image classification with TensorFlow.

Degree: Electrical and Computer Engineering, 2017, University of Texas – Austin

 Recognition of alphanumeric data using a machine learning algorithm is a problem with practical applications in license plate, traffic sign, and street number recognition. TensorFlow,… (more)

Subjects/Keywords: TensorFlow; Image recognition; Numeric image classification; Alphanumeric data recognition; Machine learning algorithms

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APA (6th Edition):

-6463-6575. (2017). Numeric image classification with TensorFlow. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/47455

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

-6463-6575. “Numeric image classification with TensorFlow.” 2017. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/47455.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

-6463-6575. “Numeric image classification with TensorFlow.” 2017. Web. 23 Apr 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-6463-6575. Numeric image classification with TensorFlow. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/47455.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-6463-6575. Numeric image classification with TensorFlow. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/47455

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

3. Watson, Lee T. Probabilistic routing-based injury avoidance navigation framework for pedalcyclists.

Degree: Electrical and Computer Engineering, 2018, University of Texas – Austin

 Easing traffic congestion in urban areas is a multifactorial challenge requiring continuous effort. Encouraging commuters to use alternative transportation such as bicycles is one simple… (more)

Subjects/Keywords: Data mining; Safety prediction; Traffic study; Crash data

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APA (6th Edition):

Watson, L. T. (2018). Probabilistic routing-based injury avoidance navigation framework for pedalcyclists. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/64214

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Watson, Lee T. “Probabilistic routing-based injury avoidance navigation framework for pedalcyclists.” 2018. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/64214.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Watson, Lee T. “Probabilistic routing-based injury avoidance navigation framework for pedalcyclists.” 2018. Web. 23 Apr 2019.

Vancouver:

Watson LT. Probabilistic routing-based injury avoidance navigation framework for pedalcyclists. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/64214.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Watson LT. Probabilistic routing-based injury avoidance navigation framework for pedalcyclists. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/64214

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

4. Mullapudi, Subhash Venkat. Distributed deep neural networks.

Degree: Electrical and Computer Engineering, 2017, University of Texas – Austin

 Deep neural networks have become popular for solving machine learning problems in the field of computer vision. Although computers have reached parity in the task… (more)

Subjects/Keywords: Distributed; Deep neural networks; Stochastic gradient descent; SGD; Artificial neural networks; ANN; DNN

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

APA (6th Edition):

Mullapudi, S. V. (2017). Distributed deep neural networks. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63752

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Mullapudi, Subhash Venkat. “Distributed deep neural networks.” 2017. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/63752.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Mullapudi, Subhash Venkat. “Distributed deep neural networks.” 2017. Web. 23 Apr 2019.

Vancouver:

Mullapudi SV. Distributed deep neural networks. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/63752.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mullapudi SV. Distributed deep neural networks. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/63752

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

5. Graves, Matthew, M.S. In Engineering. Procedural content generation of Angry Birds levels using Monte Carlo Tree Search.

Degree: Electrical and Computer Engineering, 2016, University of Texas – Austin

 Monte Carlo Tree Search is a method for searching a decision-making process, usually employed in domains such as general game playing, where an artificial intelligence… (more)

Subjects/Keywords: Monte Carlo Tree Search; MCTS; Angry Birds; Procedural; Content; Generation

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APA (6th Edition):

Graves, Matthew, M. S. I. E. (2016). Procedural content generation of Angry Birds levels using Monte Carlo Tree Search. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/46264

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Graves, Matthew, M S In Engineering. “Procedural content generation of Angry Birds levels using Monte Carlo Tree Search.” 2016. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/46264.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Graves, Matthew, M S In Engineering. “Procedural content generation of Angry Birds levels using Monte Carlo Tree Search.” 2016. Web. 23 Apr 2019.

Vancouver:

Graves, Matthew MSIE. Procedural content generation of Angry Birds levels using Monte Carlo Tree Search. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/46264.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Graves, Matthew MSIE. Procedural content generation of Angry Birds levels using Monte Carlo Tree Search. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/46264

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

6. Larcom, Ronald Craig. Foveated video compression for lossy packet networks.

Degree: Electrical and Computer Engineering, 2010, University of Texas – Austin

 Unreliable networks can severely hamper transmission of video data. In applications requiring minimal latency, video frames must be compressed using intraframe techniques. We develop a… (more)

Subjects/Keywords: Video; Compression; Foveation; Codec

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APA (6th Edition):

Larcom, R. C. (2010). Foveated video compression for lossy packet networks. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2010-12-2411

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Larcom, Ronald Craig. “Foveated video compression for lossy packet networks.” 2010. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/ETD-UT-2010-12-2411.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Larcom, Ronald Craig. “Foveated video compression for lossy packet networks.” 2010. Web. 23 Apr 2019.

Vancouver:

Larcom RC. Foveated video compression for lossy packet networks. [Internet] [Thesis]. University of Texas – Austin; 2010. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/ETD-UT-2010-12-2411.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Larcom RC. Foveated video compression for lossy packet networks. [Thesis]. University of Texas – Austin; 2010. Available from: http://hdl.handle.net/2152/ETD-UT-2010-12-2411

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

7. -5473-8890. Replication system for low power internet of things devices.

Degree: Electrical and Computer Engineering, 2017, University of Texas – Austin

 As technology evolves to where persistent and ubiquitous computing devices exist the possibilities for shared computing increases. The size and power requirements of Internet of… (more)

Subjects/Keywords: Bluetooth; BLE; Replication

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APA (6th Edition):

-5473-8890. (2017). Replication system for low power internet of things devices. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62458

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

-5473-8890. “Replication system for low power internet of things devices.” 2017. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/62458.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

-5473-8890. “Replication system for low power internet of things devices.” 2017. Web. 23 Apr 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-5473-8890. Replication system for low power internet of things devices. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/62458.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-5473-8890. Replication system for low power internet of things devices. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62458

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

8. Chen, Yudong. Learning with high-dimensional noisy data.

Degree: Electrical and Computer Engineering, 2013, University of Texas – Austin

 Learning an unknown parameter from data is a problem of fundamental importance across many fields of engineering and science. Rapid development in information technology allows… (more)

Subjects/Keywords: Robustness; Machine learning; High-dimensional statistics

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APA (6th Edition):

Chen, Y. (2013). Learning with high-dimensional noisy data. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/21318

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Chen, Yudong. “Learning with high-dimensional noisy data.” 2013. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/21318.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Chen, Yudong. “Learning with high-dimensional noisy data.” 2013. Web. 23 Apr 2019.

Vancouver:

Chen Y. Learning with high-dimensional noisy data. [Internet] [Thesis]. University of Texas – Austin; 2013. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/21318.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chen Y. Learning with high-dimensional noisy data. [Thesis]. University of Texas – Austin; 2013. Available from: http://hdl.handle.net/2152/21318

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

9. -3566-3459. Comparison of algorithms for Twitter sentiment analysis.

Degree: Electrical and Computer Engineering, 2017, University of Texas – Austin

 Sentiment Analysis has gained attention in recent years owing to the massive increase in personal statements made at the individual level, spread across vast geographic… (more)

Subjects/Keywords: Sentiment Analysis; Twitter; SVM; Naive Bayes; SGD classification

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APA (6th Edition):

-3566-3459. (2017). Comparison of algorithms for Twitter sentiment analysis. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/60372

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

-3566-3459. “Comparison of algorithms for Twitter sentiment analysis.” 2017. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/60372.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

-3566-3459. “Comparison of algorithms for Twitter sentiment analysis.” 2017. Web. 23 Apr 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-3566-3459. Comparison of algorithms for Twitter sentiment analysis. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/60372.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-3566-3459. Comparison of algorithms for Twitter sentiment analysis. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/60372

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

10. Yan, Bowei. Theoretical analysis for convex and non-convex clustering algorithms.

Degree: Statistics, 2018, University of Texas – Austin

 Clustering is one of the most important unsupervised learning problem in the machine learning and statistics community. Given a set of observations, the goal is… (more)

Subjects/Keywords: Clustering; Sub-gaussian; Mixture model; Community detection; EM algorithm; Semi-definite programming; Stochastic block model

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APA (6th Edition):

Yan, B. (2018). Theoretical analysis for convex and non-convex clustering algorithms. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68254

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Yan, Bowei. “Theoretical analysis for convex and non-convex clustering algorithms.” 2018. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/68254.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Yan, Bowei. “Theoretical analysis for convex and non-convex clustering algorithms.” 2018. Web. 23 Apr 2019.

Vancouver:

Yan B. Theoretical analysis for convex and non-convex clustering algorithms. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/68254.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yan B. Theoretical analysis for convex and non-convex clustering algorithms. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/68254

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

11. Madrid Padilla, Oscar Hernan. Constrained estimation via the fused lasso and some generalizations.

Degree: Statistics, 2017, University of Texas – Austin

 This dissertation studies structurally constrained statistical estimators. Two entwined main themes are developed: computationally efficient algorithms, and strong statistical guarantees of estimators across a wide… (more)

Subjects/Keywords: Fused lasso; Penalized likelihood.

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APA (6th Edition):

Madrid Padilla, O. H. (2017). Constrained estimation via the fused lasso and some generalizations. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63067

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Madrid Padilla, Oscar Hernan. “Constrained estimation via the fused lasso and some generalizations.” 2017. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/63067.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Madrid Padilla, Oscar Hernan. “Constrained estimation via the fused lasso and some generalizations.” 2017. Web. 23 Apr 2019.

Vancouver:

Madrid Padilla OH. Constrained estimation via the fused lasso and some generalizations. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/63067.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Madrid Padilla OH. Constrained estimation via the fused lasso and some generalizations. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/63067

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

12. Khanna, Rajiv Ashu. New perspectives and applications for greedy algorithms in machine learning.

Degree: Electrical and Computer Engineering, 2018, University of Texas – Austin

 Approximating probability densities is a core problem in Bayesian statistics, where the inference involves the computation of a posterior distribution. Variational Inference (VI) is a… (more)

Subjects/Keywords: Approximate inference; Submodularity

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APA (6th Edition):

Khanna, R. A. (2018). New perspectives and applications for greedy algorithms in machine learning. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/69183

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Khanna, Rajiv Ashu. “New perspectives and applications for greedy algorithms in machine learning.” 2018. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/69183.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Khanna, Rajiv Ashu. “New perspectives and applications for greedy algorithms in machine learning.” 2018. Web. 23 Apr 2019.

Vancouver:

Khanna RA. New perspectives and applications for greedy algorithms in machine learning. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/69183.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Khanna RA. New perspectives and applications for greedy algorithms in machine learning. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/69183

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

13. -0679-9832. Uniform positive recurrence and long term behavior of Markov decision processes, with applications in sensor scheduling.

Degree: Electrical and Computer Engineering, 2015, University of Texas – Austin

 In this dissertation, we show a number of new results relating to stability, optimal control, and value iteration algorithms for discrete-time Markov decision processes (MDPs).… (more)

Subjects/Keywords: Markov decision processes; Uniform recurrence; Harnack's inequality; Ergodic cost; Value iteration; Sensor scheduling; Intermittent observations

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APA (6th Edition):

-0679-9832. (2015). Uniform positive recurrence and long term behavior of Markov decision processes, with applications in sensor scheduling. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63871

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

-0679-9832. “Uniform positive recurrence and long term behavior of Markov decision processes, with applications in sensor scheduling.” 2015. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/63871.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

-0679-9832. “Uniform positive recurrence and long term behavior of Markov decision processes, with applications in sensor scheduling.” 2015. Web. 23 Apr 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-0679-9832. Uniform positive recurrence and long term behavior of Markov decision processes, with applications in sensor scheduling. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/63871.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-0679-9832. Uniform positive recurrence and long term behavior of Markov decision processes, with applications in sensor scheduling. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/63871

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

14. Wang, Ye, Ph. D. Novel convex optimization techniques for circuit analysis and synthesis.

Degree: Electrical and Computer Engineering, 2018, University of Texas – Austin

 Technology scaling brings about the need for computationally efficient methods for circuit analysis, optimization, and synthesis. Convex optimization is a special class of mathematical optimization… (more)

Subjects/Keywords: Convex optimization; EDA problems

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APA (6th Edition):

Wang, Ye, P. D. (2018). Novel convex optimization techniques for circuit analysis and synthesis. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/67661

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wang, Ye, Ph D. “Novel convex optimization techniques for circuit analysis and synthesis.” 2018. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/67661.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wang, Ye, Ph D. “Novel convex optimization techniques for circuit analysis and synthesis.” 2018. Web. 23 Apr 2019.

Vancouver:

Wang, Ye PD. Novel convex optimization techniques for circuit analysis and synthesis. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/67661.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang, Ye PD. Novel convex optimization techniques for circuit analysis and synthesis. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/67661

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

15. -3269-6167. Graph analytics and subset selection problems in machine learning.

Degree: Electrical and Computer Engineering, 2018, University of Texas – Austin

 In this dissertation we examine two topics relevant to modern machine learning research: 1) Subgraph counting and 2) High-dimensional subset selection. The former can be… (more)

Subjects/Keywords: Machine learning; Approximation algorithms; Graph analytics; Graph algorithms; Subset selection; Submodular optimization; Weak submodularity; Restricted strong convexity; Streaming algorithms; Interpretability

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APA (6th Edition):

-3269-6167. (2018). Graph analytics and subset selection problems in machine learning. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68499

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

-3269-6167. “Graph analytics and subset selection problems in machine learning.” 2018. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/68499.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

-3269-6167. “Graph analytics and subset selection problems in machine learning.” 2018. Web. 23 Apr 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-3269-6167. Graph analytics and subset selection problems in machine learning. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/68499.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-3269-6167. Graph analytics and subset selection problems in machine learning. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/68499

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

16. Kocaoglu, Murat. Causality : from learning to generative models.

Degree: Electrical and Computer Engineering, 2018, University of Texas – Austin

 Causality is a fundamental concept in multiple disciplines. Causal questions arise in fields ranging from medical research to engineering, philosophy to physics. The last few… (more)

Subjects/Keywords: Causality; Learning; Generative adversarial networks; Entropic

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APA (6th Edition):

Kocaoglu, M. (2018). Causality : from learning to generative models. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68646

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Kocaoglu, Murat. “Causality : from learning to generative models.” 2018. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/68646.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Kocaoglu, Murat. “Causality : from learning to generative models.” 2018. Web. 23 Apr 2019.

Vancouver:

Kocaoglu M. Causality : from learning to generative models. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/68646.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kocaoglu M. Causality : from learning to generative models. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/68646

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

17. Ye, Qiaoyang. Small cell and D2D offloading in heterogeneous cellular networks.

Degree: Electrical and Computer Engineering, 2015, University of Texas – Austin

 Future wireless networks are evolving to become ever more heterogeneous, including small cells such as picocells and femtocells, and direct device-to-device (D2D) communication that bypasses… (more)

Subjects/Keywords: Heterogeneous networks; D2D; User association; Resource allocation; Interference management; Massive MIMO

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APA (6th Edition):

Ye, Q. (2015). Small cell and D2D offloading in heterogeneous cellular networks. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/31020

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ye, Qiaoyang. “Small cell and D2D offloading in heterogeneous cellular networks.” 2015. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/31020.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ye, Qiaoyang. “Small cell and D2D offloading in heterogeneous cellular networks.” 2015. Web. 23 Apr 2019.

Vancouver:

Ye Q. Small cell and D2D offloading in heterogeneous cellular networks. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/31020.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ye Q. Small cell and D2D offloading in heterogeneous cellular networks. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/31020

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

18. -4065-8654. Resource-constrained, scalable learning.

Degree: Electrical and Computer Engineering, 2015, University of Texas – Austin

 Our unprecedented capacity for data generation and acquisition often reaches the limits of our data storage capabilities. Situations when data are generated faster or at… (more)

Subjects/Keywords: Resource contraints; Limited memory; Storage; Network; Principle component analysis; PageRank; Graph engines

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APA (6th Edition):

-4065-8654. (2015). Resource-constrained, scalable learning. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32226

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

-4065-8654. “Resource-constrained, scalable learning.” 2015. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/32226.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

-4065-8654. “Resource-constrained, scalable learning.” 2015. Web. 23 Apr 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-4065-8654. Resource-constrained, scalable learning. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/32226.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-4065-8654. Resource-constrained, scalable learning. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32226

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

19. -2165-4068. Efficient sequential probability assessment heuristic in decision analysis.

Degree: Operations Research and Industrial Engineering, 2015, University of Texas – Austin

 Many decision problems involve situations where the possible outcomes are specified but the corresponding probability mass function is only partially known. In such cases, the… (more)

Subjects/Keywords: Decision analysis; Sequential probability assessment; Partial information; SPAH; Orthogonal matching pursuit

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APA (6th Edition):

-2165-4068. (2015). Efficient sequential probability assessment heuristic in decision analysis. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/33281

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

-2165-4068. “Efficient sequential probability assessment heuristic in decision analysis.” 2015. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/33281.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

-2165-4068. “Efficient sequential probability assessment heuristic in decision analysis.” 2015. Web. 23 Apr 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-2165-4068. Efficient sequential probability assessment heuristic in decision analysis. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/33281.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-2165-4068. Efficient sequential probability assessment heuristic in decision analysis. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/33281

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

20. Puljiz, Zrinka. State reconstruction from partial observations: theory and applications.

Degree: Electrical and Computer Engineering, 2015, University of Texas – Austin

 This thesis considers the problem of signal reconstruction in the setting of partial observations. We consider this in three different contexts. First, we consider the… (more)

Subjects/Keywords: Haplotype assembly; Pilot contamination; Signal reconstruction

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APA (6th Edition):

Puljiz, Z. (2015). State reconstruction from partial observations: theory and applications. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/33333

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Puljiz, Zrinka. “State reconstruction from partial observations: theory and applications.” 2015. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/33333.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Puljiz, Zrinka. “State reconstruction from partial observations: theory and applications.” 2015. Web. 23 Apr 2019.

Vancouver:

Puljiz Z. State reconstruction from partial observations: theory and applications. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/33333.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Puljiz Z. State reconstruction from partial observations: theory and applications. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/33333

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

21. Lu, Zheng, Ph. D. Scheduling wireless transmissions exploiting application awareness and knowledge of the future.

Degree: Electrical and Computer Engineering, 2015, University of Texas – Austin

 This dissertation explores ways to improve the scheduling of wireless transmissions, by exploiting the application layer information of the ongoing transmissions and exploiting the knowledge… (more)

Subjects/Keywords: Scheduling; Wireless networking; Video delivery; Device-to-device communications

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APA (6th Edition):

Lu, Zheng, P. D. (2015). Scheduling wireless transmissions exploiting application awareness and knowledge of the future. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/33382

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Lu, Zheng, Ph D. “Scheduling wireless transmissions exploiting application awareness and knowledge of the future.” 2015. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/33382.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Lu, Zheng, Ph D. “Scheduling wireless transmissions exploiting application awareness and knowledge of the future.” 2015. Web. 23 Apr 2019.

Vancouver:

Lu, Zheng PD. Scheduling wireless transmissions exploiting application awareness and knowledge of the future. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/33382.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lu, Zheng PD. Scheduling wireless transmissions exploiting application awareness and knowledge of the future. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/33382

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

22. Daniels, Robert C. Machine learning for link adaptation in wireless networks.

Degree: Electrical and Computer Engineering, 2011, University of Texas – Austin

 Link adaptation is an important component of contemporary wireless networks that require high spectral efficiency and service a variety of network applications/configurations. By exploiting information… (more)

Subjects/Keywords: Wireless Communications; Link adaptation; Adaptive modulation and coding; Machine learning; MIMO; OFDM

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APA (6th Edition):

Daniels, R. C. (2011). Machine learning for link adaptation in wireless networks. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-12-4509

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Daniels, Robert C. “Machine learning for link adaptation in wireless networks.” 2011. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/ETD-UT-2011-12-4509.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Daniels, Robert C. “Machine learning for link adaptation in wireless networks.” 2011. Web. 23 Apr 2019.

Vancouver:

Daniels RC. Machine learning for link adaptation in wireless networks. [Internet] [Thesis]. University of Texas – Austin; 2011. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/ETD-UT-2011-12-4509.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Daniels RC. Machine learning for link adaptation in wireless networks. [Thesis]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-12-4509

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

23. -8916-5076. Post-contingency states representation and redispatch for restoration in power systems operation.

Degree: Electrical and Computer Engineering, 2018, University of Texas – Austin

 In this treatise, we will present a dynamic version of the Security Constrained Optimal Power Flow (SCOPF) problem, the "Look Ahead Security Constrained Optimal Power… (more)

Subjects/Keywords: Distributed optimization; Convex optimization; Optimal power flow (OPF); Security constrained optimal power flow (SCOPF); Look-ahead security constrained optimal power flow (LASCOPF); Alternating direction method of multipliers (ADMM); Proximal message passing (PMP); Auxiliary problem principle (APP)

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APA (6th Edition):

-8916-5076. (2018). Post-contingency states representation and redispatch for restoration in power systems operation. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63632

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

-8916-5076. “Post-contingency states representation and redispatch for restoration in power systems operation.” 2018. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/63632.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

-8916-5076. “Post-contingency states representation and redispatch for restoration in power systems operation.” 2018. Web. 23 Apr 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-8916-5076. Post-contingency states representation and redispatch for restoration in power systems operation. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/63632.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-8916-5076. Post-contingency states representation and redispatch for restoration in power systems operation. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/63632

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

24. Gunasekar, Suriya. Mining structured matrices in high dimensions.

Degree: Electrical and Computer Engineering, 2016, University of Texas – Austin

 Structured matrices refer to matrix valued data that are embedded in an inherent lower dimensional manifold with smaller degrees of freedom compared to the ambient… (more)

Subjects/Keywords: Matrix completion; High dimensional estimation; EHRs; Letor; Matrix estimation; Sample complexity

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APA (6th Edition):

Gunasekar, S. (2016). Mining structured matrices in high dimensions. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/43772

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Gunasekar, Suriya. “Mining structured matrices in high dimensions.” 2016. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/43772.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Gunasekar, Suriya. “Mining structured matrices in high dimensions.” 2016. Web. 23 Apr 2019.

Vancouver:

Gunasekar S. Mining structured matrices in high dimensions. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/43772.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Gunasekar S. Mining structured matrices in high dimensions. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/43772

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

25. Asteris, Megasthenis. Quadratic maximization under combinatorial constraints and related applications.

Degree: Electrical and Computer Engineering, 2016, University of Texas – Austin

 Motivated primarily by restricted variants of Principal Component Analysis (PCA), we study quadratic maximization problems subject to sparsity, nonnegativity and other combinatorial constraints. Intuitively, a… (more)

Subjects/Keywords: Quadratic maximization; Sparse nonnegative PCA

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APA (6th Edition):

Asteris, M. (2016). Quadratic maximization under combinatorial constraints and related applications. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/46455

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Asteris, Megasthenis. “Quadratic maximization under combinatorial constraints and related applications.” 2016. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/46455.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Asteris, Megasthenis. “Quadratic maximization under combinatorial constraints and related applications.” 2016. Web. 23 Apr 2019.

Vancouver:

Asteris M. Quadratic maximization under combinatorial constraints and related applications. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/46455.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Asteris M. Quadratic maximization under combinatorial constraints and related applications. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/46455

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

26. Yi, Xinyang. Learning with latent structures, robustness and non-linearity : non-convex approaches.

Degree: Electrical and Computer Engineering, 2016, University of Texas – Austin

 Non-convex optimization based algorithms are ubiquitous in machine learning and statistical estimation, especially in dealing with complex models that are noisy, non-linear or contain latent… (more)

Subjects/Keywords: Statistical machine learning; High dimensional statistics; Non-convex optimization; Mixed linear regression

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APA (6th Edition):

Yi, X. (2016). Learning with latent structures, robustness and non-linearity : non-convex approaches. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/46474

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Yi, Xinyang. “Learning with latent structures, robustness and non-linearity : non-convex approaches.” 2016. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/46474.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Yi, Xinyang. “Learning with latent structures, robustness and non-linearity : non-convex approaches.” 2016. Web. 23 Apr 2019.

Vancouver:

Yi X. Learning with latent structures, robustness and non-linearity : non-convex approaches. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/46474.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yi X. Learning with latent structures, robustness and non-linearity : non-convex approaches. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/46474

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

27. -3807-0843. Transmission Expansion Planning : computational challenges toward real-size networks.

Degree: Electrical and Computer Engineering, 2017, University of Texas – Austin

 The importance of the transmission network for supplying electricity demand is undeniable, and Transmission Expansion Planning (TEP) studies is key for a reliable power system.… (more)

Subjects/Keywords: Transmission Expansion Planning; Stochastic optimization; Mixed integer programming; Benders decomposition; Progressive hedging

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APA (6th Edition):

-3807-0843. (2017). Transmission Expansion Planning : computational challenges toward real-size networks. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62068

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

-3807-0843. “Transmission Expansion Planning : computational challenges toward real-size networks.” 2017. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/62068.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

-3807-0843. “Transmission Expansion Planning : computational challenges toward real-size networks.” 2017. Web. 23 Apr 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-3807-0843. Transmission Expansion Planning : computational challenges toward real-size networks. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/62068.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-3807-0843. Transmission Expansion Planning : computational challenges toward real-size networks. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62068

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

28. -7845-7631. Graph theoretic results on index coding, causal inference and learning graphical models.

Degree: Electrical and Computer Engineering, 2016, University of Texas – Austin

 Exploiting and learning graph structures is becoming ubiquitous in Network Information Theory and Machine Learning. The former deals with efficient communication schemes in a many-node… (more)

Subjects/Keywords: Index coding; Causal inference; Information theory; Graphical models

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APA (6th Edition):

-7845-7631. (2016). Graph theoretic results on index coding, causal inference and learning graphical models. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/44041

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

-7845-7631. “Graph theoretic results on index coding, causal inference and learning graphical models.” 2016. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/44041.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

-7845-7631. “Graph theoretic results on index coding, causal inference and learning graphical models.” 2016. Web. 23 Apr 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-7845-7631. Graph theoretic results on index coding, causal inference and learning graphical models. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/44041.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-7845-7631. Graph theoretic results on index coding, causal inference and learning graphical models. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/44041

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

29. Park, Dohyung. Efficient non-convex algorithms for large-scale learning problems.

Degree: Electrical and Computer Engineering, 2016, University of Texas – Austin

 The emergence of modern large-scale datasets has led to a huge interest in the problem of learning hidden complex structures. Not only can models from… (more)

Subjects/Keywords: Machine learning; Non-convex optimization

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

APA (6th Edition):

Park, D. (2016). Efficient non-convex algorithms for large-scale learning problems. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/46581

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Park, Dohyung. “Efficient non-convex algorithms for large-scale learning problems.” 2016. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/46581.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Park, Dohyung. “Efficient non-convex algorithms for large-scale learning problems.” 2016. Web. 23 Apr 2019.

Vancouver:

Park D. Efficient non-convex algorithms for large-scale learning problems. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/46581.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Park D. Efficient non-convex algorithms for large-scale learning problems. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/46581

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

30. Koc, Ali. Prioritization via stochastic optimization.

Degree: Mechanical Engineering, 2010, University of Texas – Austin

 We take a novel perspective on real-life decision making problems involving binary activity-selection decisions that compete for scarce resources. The current literature in operations research… (more)

Subjects/Keywords: Prioritization; Stochastic programming; Branch-and-price; Parallel programming; Operations research; Nuclear power industry; Parallelization

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

APA (6th Edition):

Koc, A. (2010). Prioritization via stochastic optimization. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2010-05-1258

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Koc, Ali. “Prioritization via stochastic optimization.” 2010. Thesis, University of Texas – Austin. Accessed April 23, 2019. http://hdl.handle.net/2152/ETD-UT-2010-05-1258.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Koc, Ali. “Prioritization via stochastic optimization.” 2010. Web. 23 Apr 2019.

Vancouver:

Koc A. Prioritization via stochastic optimization. [Internet] [Thesis]. University of Texas – Austin; 2010. [cited 2019 Apr 23]. Available from: http://hdl.handle.net/2152/ETD-UT-2010-05-1258.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Koc A. Prioritization via stochastic optimization. [Thesis]. University of Texas – Austin; 2010. Available from: http://hdl.handle.net/2152/ETD-UT-2010-05-1258

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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