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You searched for +publisher:"University of Texas – Austin" +contributor:("Ravikumar, Pradeep"). Showing records 1 – 28 of 28 total matches.

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University of Texas – Austin

1. Yang, Eunho. High-dimensional statistics : model specification and elementary estimators.

Degree: Computer Sciences, 2014, University of Texas – Austin

 Modern statistics typically deals with complex data, in particular where the ambient dimension of the problem p may be of the same order as, or… (more)

Subjects/Keywords: High-dimensional statistics; Markov random fields; Graphical models; Closed-form estimators

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

APA (6th Edition):

Yang, E. (2014). High-dimensional statistics : model specification and elementary estimators. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/28058

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):

Yang, Eunho. “High-dimensional statistics : model specification and elementary estimators.” 2014. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/28058.

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

MLA Handbook (7th Edition):

Yang, Eunho. “High-dimensional statistics : model specification and elementary estimators.” 2014. Web. 21 Apr 2019.

Vancouver:

Yang E. High-dimensional statistics : model specification and elementary estimators. [Internet] [Thesis]. University of Texas – Austin; 2014. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/28058.

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

Council of Science Editors:

Yang E. High-dimensional statistics : model specification and elementary estimators. [Thesis]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/28058

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


University of Texas – Austin

2. Johnson, Christopher Carroll. Greedy structure learning of Markov Random Fields.

Degree: Computer Sciences, 2011, University of Texas – Austin

 Probabilistic graphical models are used in a variety of domains to capture and represent general dependencies in joint probability distributions. In this document we examine… (more)

Subjects/Keywords: Machine learning; Graphical models; Markov Random Fields; Structure learning; Probability; Uncertainty; Greedy algorithms

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

APA (6th Edition):

Johnson, C. C. (2011). Greedy structure learning of Markov Random Fields. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-08-4331

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):

Johnson, Christopher Carroll. “Greedy structure learning of Markov Random Fields.” 2011. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/ETD-UT-2011-08-4331.

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

MLA Handbook (7th Edition):

Johnson, Christopher Carroll. “Greedy structure learning of Markov Random Fields.” 2011. Web. 21 Apr 2019.

Vancouver:

Johnson CC. Greedy structure learning of Markov Random Fields. [Internet] [Thesis]. University of Texas – Austin; 2011. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/ETD-UT-2011-08-4331.

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

Council of Science Editors:

Johnson CC. Greedy structure learning of Markov Random Fields. [Thesis]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-08-4331

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


University of Texas – Austin

3. Tandon, Rashish. On structured and distributed learning.

Degree: Computer Sciences, 2018, University of Texas – Austin

 With the growth in size and complexity of data, methods exploiting low-dimensional structure, as well as distributed methods, have been playing an ever important role… (more)

Subjects/Keywords: Machine learning; Graphical models; Ising models; Neighborhood selection; Regression; Linear regression; Kernel ridge regression; Local learning; Stragglers; Coding theory; Gradient coding

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APA (6th Edition):

Tandon, R. (2018). On structured and distributed learning. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68175

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):

Tandon, Rashish. “On structured and distributed learning.” 2018. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/68175.

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

MLA Handbook (7th Edition):

Tandon, Rashish. “On structured and distributed learning.” 2018. Web. 21 Apr 2019.

Vancouver:

Tandon R. On structured and distributed learning. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/68175.

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

Council of Science Editors:

Tandon R. On structured and distributed learning. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/68175

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

4. Yang, Jisong. Using greedy algorithm to learn graphical model for digit recognition.

Degree: Statistics, 2014, University of Texas – Austin

 Graphical model, the marriage between graph theory and probability theory, has been drawing increasing attention because of its many attractive features. In this paper, we… (more)

Subjects/Keywords: Graphical model; Markov random field

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

APA (6th Edition):

Yang, J. (2014). Using greedy algorithm to learn graphical model for digit recognition. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/28131

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):

Yang, Jisong. “Using greedy algorithm to learn graphical model for digit recognition.” 2014. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/28131.

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

MLA Handbook (7th Edition):

Yang, Jisong. “Using greedy algorithm to learn graphical model for digit recognition.” 2014. Web. 21 Apr 2019.

Vancouver:

Yang J. Using greedy algorithm to learn graphical model for digit recognition. [Internet] [Thesis]. University of Texas – Austin; 2014. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/28131.

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

Council of Science Editors:

Yang J. Using greedy algorithm to learn graphical model for digit recognition. [Thesis]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/28131

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


University of Texas – Austin

5. -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 21, 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. 21 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 21]. 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

6. -6435-245X. Learning with positive and unlabeled examples.

Degree: Computer Sciences, 2015, University of Texas – Austin

 Developing partially supervised models is becoming increasingly relevant in the context of modern machine learning applications, where supervision often comes at a cost. In particular,… (more)

Subjects/Keywords: PU learning; Learning theory

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APA (6th Edition):

-6435-245X. (2015). Learning with positive and unlabeled examples. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32826

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):

-6435-245X. “Learning with positive and unlabeled examples.” 2015. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/32826.

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):

-6435-245X. “Learning with positive and unlabeled examples.” 2015. Web. 21 Apr 2019.

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

Vancouver:

-6435-245X. Learning with positive and unlabeled examples. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/32826.

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:

-6435-245X. Learning with positive and unlabeled examples. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32826

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

7. Das, Shreepriya. Algorithms for next generation sequencing data analysis.

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

 The field of genomics has witnessed tremendous achievements in the past two decades. The advances in sequencing technology have enabled acquisition of massive amounts of… (more)

Subjects/Keywords: Basecalling; Haplotyping; Bioinformatics; Computational biology

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APA (6th Edition):

Das, S. (2015). Algorithms for next generation sequencing data analysis. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/33328

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):

Das, Shreepriya. “Algorithms for next generation sequencing data analysis.” 2015. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/33328.

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

MLA Handbook (7th Edition):

Das, Shreepriya. “Algorithms for next generation sequencing data analysis.” 2015. Web. 21 Apr 2019.

Vancouver:

Das S. Algorithms for next generation sequencing data analysis. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/33328.

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

Council of Science Editors:

Das S. Algorithms for next generation sequencing data analysis. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/33328

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


University of Texas – Austin

8. -0782-4953. Robust network compressive sensing.

Degree: Computer Sciences, 2015, University of Texas – Austin

 Networks are constantly generating an enormous amount of rich and diverse information. Such information creates exciting opportunities for network analytics and provides deep insights into… (more)

Subjects/Keywords: Compressive sensing; Big data

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

APA (6th Edition):

-0782-4953. (2015). Robust network compressive sensing. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/38181

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):

-0782-4953. “Robust network compressive sensing.” 2015. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/38181.

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):

-0782-4953. “Robust network compressive sensing.” 2015. Web. 21 Apr 2019.

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

Vancouver:

-0782-4953. Robust network compressive sensing. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/38181.

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:

-0782-4953. Robust network compressive sensing. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/38181

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

9. Huynh, Tuyen Ngoc. Improving the accuracy and scalability of discriminative learning methods for Markov logic networks.

Degree: Computer Sciences, 2011, University of Texas – Austin

 Many real-world problems involve data that both have complex structures and uncertainty. Statistical relational learning (SRL) is an emerging area of research that addresses the… (more)

Subjects/Keywords: Markov logic networks; Statistical relational learning; Structured prediction; Logic networks; Artificial intelligence; Machine learning

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

APA (6th Edition):

Huynh, T. N. (2011). Improving the accuracy and scalability of discriminative learning methods for Markov logic networks. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-05-3436

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):

Huynh, Tuyen Ngoc. “Improving the accuracy and scalability of discriminative learning methods for Markov logic networks.” 2011. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/ETD-UT-2011-05-3436.

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

MLA Handbook (7th Edition):

Huynh, Tuyen Ngoc. “Improving the accuracy and scalability of discriminative learning methods for Markov logic networks.” 2011. Web. 21 Apr 2019.

Vancouver:

Huynh TN. Improving the accuracy and scalability of discriminative learning methods for Markov logic networks. [Internet] [Thesis]. University of Texas – Austin; 2011. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/ETD-UT-2011-05-3436.

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

Council of Science Editors:

Huynh TN. Improving the accuracy and scalability of discriminative learning methods for Markov logic networks. [Thesis]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-05-3436

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


University of Texas – Austin

10. https://orcid.org/0000-0002-8791-7828. Listwise frameworks for ranking and rank aggregation.

Degree: Computer Sciences, 2018, University of Texas – Austin

 The goal in Learning to Rank (LETOR) is to learn to order a novel set of items, given training data comprising sets of items and… (more)

Subjects/Keywords: Machine learning; Ranking; Learning to rank; Rank aggregation; Listwise methods; Tracking

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APA (6th Edition):

https://orcid.org/0000-0002-8791-7828. (2018). Listwise frameworks for ranking and rank aggregation. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63694

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):

https://orcid.org/0000-0002-8791-7828. “Listwise frameworks for ranking and rank aggregation.” 2018. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/63694.

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):

https://orcid.org/0000-0002-8791-7828. “Listwise frameworks for ranking and rank aggregation.” 2018. Web. 21 Apr 2019.

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

Vancouver:

https://orcid.org/0000-0002-8791-7828. Listwise frameworks for ranking and rank aggregation. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/63694.

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:

https://orcid.org/0000-0002-8791-7828. Listwise frameworks for ranking and rank aggregation. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/63694

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

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

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 21, 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. 21 Apr 2019.

Vancouver:

Gunasekar S. Mining structured matrices in high dimensions. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Apr 21]. 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

12. Garrette, Daniel Hunter. Inducing grammars from linguistic universals and realistic amounts of supervision.

Degree: Computer Sciences, 2015, University of Texas – Austin

 The best performing NLP models to date are learned from large volumes of manually-annotated data. For tasks like part-of-speech tagging and grammatical parsing, high performance… (more)

Subjects/Keywords: Computer science; Artificial intelligence; Natural language processing; Machine learning; Bayesian statistics; Grammar induction; Parsing; Computational linguistics

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APA (6th Edition):

Garrette, D. H. (2015). Inducing grammars from linguistic universals and realistic amounts of supervision. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/44478

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):

Garrette, Daniel Hunter. “Inducing grammars from linguistic universals and realistic amounts of supervision.” 2015. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/44478.

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

MLA Handbook (7th Edition):

Garrette, Daniel Hunter. “Inducing grammars from linguistic universals and realistic amounts of supervision.” 2015. Web. 21 Apr 2019.

Vancouver:

Garrette DH. Inducing grammars from linguistic universals and realistic amounts of supervision. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/44478.

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

Council of Science Editors:

Garrette DH. Inducing grammars from linguistic universals and realistic amounts of supervision. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/44478

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

13. Bayzid, Md. Shamsuzzoha. Estimating species trees from gene trees despite gene tree incongruence under realistic model conditions.

Degree: Computer Sciences, 2016, University of Texas – Austin

 Species tree estimation is frequently based on phylogenomic approaches that use multiple genes from throughout the genome. With the rapid growth rate of newly sequenced… (more)

Subjects/Keywords: Phylogenetic tree; Species tree; Gene tree; Incomplete lineage sorting (ILS); Deep coalescence; Gene duplication and loss; Multi-species coalescent model; Phylogenomic analyses; Gene tree discordance

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APA (6th Edition):

Bayzid, M. S. (2016). Estimating species trees from gene trees despite gene tree incongruence under realistic model conditions. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/46404

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):

Bayzid, Md Shamsuzzoha. “Estimating species trees from gene trees despite gene tree incongruence under realistic model conditions.” 2016. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/46404.

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

MLA Handbook (7th Edition):

Bayzid, Md Shamsuzzoha. “Estimating species trees from gene trees despite gene tree incongruence under realistic model conditions.” 2016. Web. 21 Apr 2019.

Vancouver:

Bayzid MS. Estimating species trees from gene trees despite gene tree incongruence under realistic model conditions. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/46404.

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

Council of Science Editors:

Bayzid MS. Estimating species trees from gene trees despite gene tree incongruence under realistic model conditions. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/46404

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


University of Texas – Austin

14. -4493-3358. Appropriate, accessible and appealing probabilistic graphical models.

Degree: Computer Sciences, 2017, University of Texas – Austin

 Appropriate - Many multivariate probabilistic models either use independent distributions or dependent Gaussian distributions. Yet, many real-world datasets contain count-valued or non-negative skewed data, e.g.… (more)

Subjects/Keywords: Graphical models; Topic models; Poisson; Count data; Visualization; Human computer interaction

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APA (6th Edition):

-4493-3358. (2017). Appropriate, accessible and appealing probabilistic graphical models. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62986

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):

-4493-3358. “Appropriate, accessible and appealing probabilistic graphical models.” 2017. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/62986.

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):

-4493-3358. “Appropriate, accessible and appealing probabilistic graphical models.” 2017. Web. 21 Apr 2019.

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

Vancouver:

-4493-3358. Appropriate, accessible and appealing probabilistic graphical models. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/62986.

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:

-4493-3358. Appropriate, accessible and appealing probabilistic graphical models. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62986

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

15. -1624-1733. Exploiting structure in large-scale optimization for machine learning.

Degree: Computer Sciences, 2015, University of Texas – Austin

 With an immense growth of data, there is a great need for solving large-scale machine learning problems. Classical optimization algorithms usually cannot scale up due… (more)

Subjects/Keywords: Machine learning; Optimization

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APA (6th Edition):

-1624-1733. (2015). Exploiting structure in large-scale optimization for machine learning. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/31381

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):

-1624-1733. “Exploiting structure in large-scale optimization for machine learning.” 2015. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/31381.

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):

-1624-1733. “Exploiting structure in large-scale optimization for machine learning.” 2015. Web. 21 Apr 2019.

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

Vancouver:

-1624-1733. Exploiting structure in large-scale optimization for machine learning. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/31381.

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:

-1624-1733. Exploiting structure in large-scale optimization for machine learning. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/31381

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

16. Urieli, Daniel. Autonomous trading in modern electricity markets.

Degree: Computer Sciences, 2015, University of Texas – Austin

 The smart grid is an electricity grid augmented with digital technologies that automate the management of electricity delivery. The smart grid is envisioned to be… (more)

Subjects/Keywords: Autonomous electricity trading; Smart grid; Electricity markets; Power markets; Planning; Lookahead planning; Monte-Carlo planning; Learning agents; Embedded machine learning; Machine learning; Reinforcement learning

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APA (6th Edition):

Urieli, D. (2015). Autonomous trading in modern electricity markets. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/39597

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):

Urieli, Daniel. “Autonomous trading in modern electricity markets.” 2015. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/39597.

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

MLA Handbook (7th Edition):

Urieli, Daniel. “Autonomous trading in modern electricity markets.” 2015. Web. 21 Apr 2019.

Vancouver:

Urieli D. Autonomous trading in modern electricity markets. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/39597.

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

Council of Science Editors:

Urieli D. Autonomous trading in modern electricity markets. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/39597

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


University of Texas – Austin

17. -8689-6770. Strong lower bounds on generic convex relaxations.

Degree: Computer Sciences, 2016, University of Texas – Austin

 Despite significant successes in understanding the hardness of computational problems based on standard assumptions such as P != NP, there are important settings where the… (more)

Subjects/Keywords: Lower bounds; Semidefinite programming; Planted clique; Constraint satisfaction

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APA (6th Edition):

-8689-6770. (2016). Strong lower bounds on generic convex relaxations. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/44040

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):

-8689-6770. “Strong lower bounds on generic convex relaxations.” 2016. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/44040.

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):

-8689-6770. “Strong lower bounds on generic convex relaxations.” 2016. Web. 21 Apr 2019.

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

Vancouver:

-8689-6770. Strong lower bounds on generic convex relaxations. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/44040.

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:

-8689-6770. Strong lower bounds on generic convex relaxations. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/44040

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

18. Beltagy, Islam Kamel Ahmed. Natural language semantics using probabilistic logic.

Degree: Computer Sciences, 2016, University of Texas – Austin

 With better natural language semantic representations, computers can do more applications more efficiently as a result of better understanding of natural text. However, no single… (more)

Subjects/Keywords: NLP; Machine learning; Probabilistic logic; MLNs; PSL; Markov Logic Network; QA; RTE; STS

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APA (6th Edition):

Beltagy, I. K. A. (2016). Natural language semantics using probabilistic logic. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/46617

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):

Beltagy, Islam Kamel Ahmed. “Natural language semantics using probabilistic logic.” 2016. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/46617.

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

MLA Handbook (7th Edition):

Beltagy, Islam Kamel Ahmed. “Natural language semantics using probabilistic logic.” 2016. Web. 21 Apr 2019.

Vancouver:

Beltagy IKA. Natural language semantics using probabilistic logic. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/46617.

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

Council of Science Editors:

Beltagy IKA. Natural language semantics using probabilistic logic. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/46617

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


University of Texas – Austin

19. Bhojanapalli, Venkata Sesha Pavana Srinadh. Large scale matrix factorization with guarantees: sampling and bi-linearity.

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

 Low rank matrix factorization is an important step in many high dimensional machine learning algorithms. Traditional algorithms for factorization do not scale well with the… (more)

Subjects/Keywords: Matrix completion; Non-convex optimization; Low rank approximation; Semi-definite optimization; Tensor factorization; Scalable algorithms

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APA (6th Edition):

Bhojanapalli, V. S. P. S. (2015). Large scale matrix factorization with guarantees: sampling and bi-linearity. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32832

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):

Bhojanapalli, Venkata Sesha Pavana Srinadh. “Large scale matrix factorization with guarantees: sampling and bi-linearity.” 2015. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/32832.

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

MLA Handbook (7th Edition):

Bhojanapalli, Venkata Sesha Pavana Srinadh. “Large scale matrix factorization with guarantees: sampling and bi-linearity.” 2015. Web. 21 Apr 2019.

Vancouver:

Bhojanapalli VSPS. Large scale matrix factorization with guarantees: sampling and bi-linearity. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/32832.

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

Council of Science Editors:

Bhojanapalli VSPS. Large scale matrix factorization with guarantees: sampling and bi-linearity. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32832

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


University of Texas – Austin

20. -0511-240X. Efficient approaches in network inference.

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

 Network based inference is almost ubiquitous in modern machine learning applications. In this dissertation we investigate several such problems motivated by applications in social networks,… (more)

Subjects/Keywords: Network inference; Graphical model; Epidemic cascade; Community detection; Mixture models; Side information; Semi-supervised

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APA (6th Edition):

-0511-240X. (2016). Efficient approaches in network inference. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/46366

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):

-0511-240X. “Efficient approaches in network inference.” 2016. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/46366.

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):

-0511-240X. “Efficient approaches in network inference.” 2016. Web. 21 Apr 2019.

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

Vancouver:

-0511-240X. Efficient approaches in network inference. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/46366.

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:

-0511-240X. Efficient approaches in network inference. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/46366

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

21. Hutton, Amanda Rachel. Using sentence-level classification to predict sentiment at the document-level.

Degree: Statistics, 2012, University of Texas – Austin

 This report explores various aspects of sentiment mining. The two research goals for the report were: (1) to determine useful methods in increasing recall of… (more)

Subjects/Keywords: Sentiment mining; Sentence-level classification; Text classification; Recall

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APA (6th Edition):

Hutton, A. R. (2012). Using sentence-level classification to predict sentiment at the document-level. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2012-05-5553

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):

Hutton, Amanda Rachel. “Using sentence-level classification to predict sentiment at the document-level.” 2012. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/ETD-UT-2012-05-5553.

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

MLA Handbook (7th Edition):

Hutton, Amanda Rachel. “Using sentence-level classification to predict sentiment at the document-level.” 2012. Web. 21 Apr 2019.

Vancouver:

Hutton AR. Using sentence-level classification to predict sentiment at the document-level. [Internet] [Thesis]. University of Texas – Austin; 2012. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5553.

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

Council of Science Editors:

Hutton AR. Using sentence-level classification to predict sentiment at the document-level. [Thesis]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5553

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

22. Meng, Qingchao. Trading Strategies back test on crude oil future contracts with time series modeling.

Degree: Statistics, 2012, University of Texas – Austin

 This report examines two trading strategies on crude oil futures contracts by employing four time series models. Using daily prices of crude oil futures contracts… (more)

Subjects/Keywords: Time models; Energy commodity; Financial econometrics

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APA (6th Edition):

Meng, Q. (2012). Trading Strategies back test on crude oil future contracts with time series modeling. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2012-08-4323

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):

Meng, Qingchao. “Trading Strategies back test on crude oil future contracts with time series modeling.” 2012. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/ETD-UT-2012-08-4323.

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

MLA Handbook (7th Edition):

Meng, Qingchao. “Trading Strategies back test on crude oil future contracts with time series modeling.” 2012. Web. 21 Apr 2019.

Vancouver:

Meng Q. Trading Strategies back test on crude oil future contracts with time series modeling. [Internet] [Thesis]. University of Texas – Austin; 2012. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/ETD-UT-2012-08-4323.

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

Council of Science Editors:

Meng Q. Trading Strategies back test on crude oil future contracts with time series modeling. [Thesis]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/ETD-UT-2012-08-4323

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

23. Silverthorn, Bryan Connor. A probabilistic architecture for algorithm portfolios.

Degree: Computer Sciences, 2012, University of Texas – Austin

 Heuristic algorithms for logical reasoning are increasingly successful on computationally difficult problems such as satisfiability, and these solvers enable applications from circuit verification to software… (more)

Subjects/Keywords: Artificial intelligence; Machine learning; Propositional logic; Algorithm selection; Algorithm portfolios; Satisfiability

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APA (6th Edition):

Silverthorn, B. C. (2012). A probabilistic architecture for algorithm portfolios. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/19828

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):

Silverthorn, Bryan Connor. “A probabilistic architecture for algorithm portfolios.” 2012. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/19828.

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

MLA Handbook (7th Edition):

Silverthorn, Bryan Connor. “A probabilistic architecture for algorithm portfolios.” 2012. Web. 21 Apr 2019.

Vancouver:

Silverthorn BC. A probabilistic architecture for algorithm portfolios. [Internet] [Thesis]. University of Texas – Austin; 2012. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/19828.

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

Council of Science Editors:

Silverthorn BC. A probabilistic architecture for algorithm portfolios. [Thesis]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/19828

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

24. Chakraborty, Doran. Sample efficient multiagent learning in the presence of Markovian agents.

Degree: Computer Sciences, 2012, University of Texas – Austin

 The problem of multiagent learning (or MAL) is concerned with the study of how agents can learn and adapt in the presence of other agents… (more)

Subjects/Keywords: Artificial intelligence; Multiagent learning

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APA (6th Edition):

Chakraborty, D. (2012). Sample efficient multiagent learning in the presence of Markovian agents. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/19459

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):

Chakraborty, Doran. “Sample efficient multiagent learning in the presence of Markovian agents.” 2012. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/19459.

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

MLA Handbook (7th Edition):

Chakraborty, Doran. “Sample efficient multiagent learning in the presence of Markovian agents.” 2012. Web. 21 Apr 2019.

Vancouver:

Chakraborty D. Sample efficient multiagent learning in the presence of Markovian agents. [Internet] [Thesis]. University of Texas – Austin; 2012. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/19459.

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

Council of Science Editors:

Chakraborty D. Sample efficient multiagent learning in the presence of Markovian agents. [Thesis]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/19459

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

25. Lockett, Alan Justin. General-purpose optimization through information maximization.

Degree: Computer Sciences, 2012, University of Texas – Austin

 The primary goal of artificial intelligence research is to develop a machine capable of learning to solve disparate real-world tasks autonomously, without relying on specialized… (more)

Subjects/Keywords: Optimization; General-purpose learning; Martingale optimization; Artificial intelligence; Evolutionary computation; Genetic algorithms; Simulated annealing; Evolutionary annealing; Neuroannealing; Neural networks; Neural network controllers; Neuroevolution; Differential evolution; No Free Lunch theorems; NFL Identification Theorem; Population-based stochastic optimization; Iterative optimization; Optimal optimization; Information-maximization principle; Convex control; Algorithm selection

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APA (6th Edition):

Lockett, A. J. (2012). General-purpose optimization through information maximization. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2012-05-5459

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):

Lockett, Alan Justin. “General-purpose optimization through information maximization.” 2012. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/ETD-UT-2012-05-5459.

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

MLA Handbook (7th Edition):

Lockett, Alan Justin. “General-purpose optimization through information maximization.” 2012. Web. 21 Apr 2019.

Vancouver:

Lockett AJ. General-purpose optimization through information maximization. [Internet] [Thesis]. University of Texas – Austin; 2012. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5459.

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

Council of Science Editors:

Lockett AJ. General-purpose optimization through information maximization. [Thesis]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5459

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

26. Vijaya Raghavan, Sindhu. Bayesian Logic Programs for plan recognition and machine reading.

Degree: Computer Sciences, 2012, University of Texas – Austin

 Several real world tasks involve data that is uncertain and relational in nature. Traditional approaches like first-order logic and probabilistic models either deal with structured… (more)

Subjects/Keywords: Bayesian Logic Programs; Statistical relational learning; Plan recognition; Abductive reasoning; Machine reading; Rule learning; Information extraction; BLPs; SRL; IE; BALPs; Bayesian Abductive Logic Programs

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APA (6th Edition):

Vijaya Raghavan, S. (2012). Bayesian Logic Programs for plan recognition and machine reading. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/19544

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):

Vijaya Raghavan, Sindhu. “Bayesian Logic Programs for plan recognition and machine reading.” 2012. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/19544.

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

MLA Handbook (7th Edition):

Vijaya Raghavan, Sindhu. “Bayesian Logic Programs for plan recognition and machine reading.” 2012. Web. 21 Apr 2019.

Vancouver:

Vijaya Raghavan S. Bayesian Logic Programs for plan recognition and machine reading. [Internet] [Thesis]. University of Texas – Austin; 2012. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/19544.

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

Council of Science Editors:

Vijaya Raghavan S. Bayesian Logic Programs for plan recognition and machine reading. [Thesis]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/19544

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

27. Saggar, Manish. Computational analysis of meditation.

Degree: Computer Sciences, 2011, University of Texas – Austin

 Meditation training has been shown to improve attention and emotion regulation. However, the mechanisms responsible for these effects are largely unknown. In order to make… (more)

Subjects/Keywords: Computational modeling; Meditation; EEG; Sustained attention; Spectral analysis; Preprocessing

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APA (6th Edition):

Saggar, M. (2011). Computational analysis of meditation. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-08-3964

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):

Saggar, Manish. “Computational analysis of meditation.” 2011. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/ETD-UT-2011-08-3964.

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

MLA Handbook (7th Edition):

Saggar, Manish. “Computational analysis of meditation.” 2011. Web. 21 Apr 2019.

Vancouver:

Saggar M. Computational analysis of meditation. [Internet] [Thesis]. University of Texas – Austin; 2011. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/ETD-UT-2011-08-3964.

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

Council of Science Editors:

Saggar M. Computational analysis of meditation. [Thesis]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-08-3964

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

28. Jalali, Ali, 1982-. Dirty statistical models.

Degree: Electrical and Computer Engineering, 2012, University of Texas – Austin

 In fields across science and engineering, we are increasingly faced with problems where the number of variables or features we need to estimate is much… (more)

Subjects/Keywords: Structure learning; Statistical inference; Dirty models; High-dimensional statistics; Machine learning; Sparse and low-rank decomposition; Graph clustering; Time series analysis; Greedy dirty algorithms

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APA (6th Edition):

Jalali, Ali, 1. (2012). Dirty statistical models. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2012-05-5088

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):

Jalali, Ali, 1982-. “Dirty statistical models.” 2012. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/ETD-UT-2012-05-5088.

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

MLA Handbook (7th Edition):

Jalali, Ali, 1982-. “Dirty statistical models.” 2012. Web. 21 Apr 2019.

Vancouver:

Jalali, Ali 1. Dirty statistical models. [Internet] [Thesis]. University of Texas – Austin; 2012. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5088.

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

Council of Science Editors:

Jalali, Ali 1. Dirty statistical models. [Thesis]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5088

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

.