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You searched for +publisher:"University of Texas – Austin" +contributor:("Price, Eric"). Showing records 1 – 10 of 10 total matches.

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University of Texas – Austin

1. Chen, Xue, Ph. D. Using and saving randomness.

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

 Randomness is ubiquitous and exceedingly useful in computer science. For example, in sparse recovery, randomized algorithms are more efficient and robust than their deterministic counterparts.… (more)

Subjects/Keywords: Query complexity; Linear families; Spectral sparsification; Active regression; Sparse Fourier transform; Hash families; Randomness extractor; Chaining argument; Multiple-choice schemes

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

APA (6th Edition):

Chen, Xue, P. D. (2018). Using and saving randomness. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/65850

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, Xue, Ph D. “Using and saving randomness.” 2018. Thesis, University of Texas – Austin. Accessed March 21, 2019. http://hdl.handle.net/2152/65850.

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

MLA Handbook (7th Edition):

Chen, Xue, Ph D. “Using and saving randomness.” 2018. Web. 21 Mar 2019.

Vancouver:

Chen, Xue PD. Using and saving randomness. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/2152/65850.

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

Council of Science Editors:

Chen, Xue PD. Using and saving randomness. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/65850

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


University of Texas – Austin

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

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 March 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 Mar 2019.

Vancouver:

Tandon R. On structured and distributed learning. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Mar 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


University of Texas – Austin

3. -1410-4981. Unit-demand auctions : fast algorithms for special cases and a connection to stable marriage with indifferences.

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

 Unit-demand auctions have been heavily studied, in part because this model allows for a mechanism enjoying a remarkably strong combination of game-theoretic properties: efficiency, stability… (more)

Subjects/Keywords: Unit-demand auctions; VCG mechanism; Stable marriage; Strategyproofness

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APA (6th Edition):

-1410-4981. (2017). Unit-demand auctions : fast algorithms for special cases and a connection to stable marriage with indifferences. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/61907

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):

-1410-4981. “Unit-demand auctions : fast algorithms for special cases and a connection to stable marriage with indifferences.” 2017. Thesis, University of Texas – Austin. Accessed March 21, 2019. http://hdl.handle.net/2152/61907.

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):

-1410-4981. “Unit-demand auctions : fast algorithms for special cases and a connection to stable marriage with indifferences.” 2017. Web. 21 Mar 2019.

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

Vancouver:

-1410-4981. Unit-demand auctions : fast algorithms for special cases and a connection to stable marriage with indifferences. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/2152/61907.

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:

-1410-4981. Unit-demand auctions : fast algorithms for special cases and a connection to stable marriage with indifferences. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/61907

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

4. Bhowmick, Abhishek. Algebraic and analytic techniques in coding theory.

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

 Error correcting codes are designed to tackle the problem of reliable trans- mission of data through noisy channels. A major challenge in coding theory is… (more)

Subjects/Keywords: Polynomials; Coding theory

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

APA (6th Edition):

Bhowmick, A. (2015). Algebraic and analytic techniques in coding theory. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/33308

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):

Bhowmick, Abhishek. “Algebraic and analytic techniques in coding theory.” 2015. Thesis, University of Texas – Austin. Accessed March 21, 2019. http://hdl.handle.net/2152/33308.

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

MLA Handbook (7th Edition):

Bhowmick, Abhishek. “Algebraic and analytic techniques in coding theory.” 2015. Web. 21 Mar 2019.

Vancouver:

Bhowmick A. Algebraic and analytic techniques in coding theory. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/2152/33308.

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

Council of Science Editors:

Bhowmick A. Algebraic and analytic techniques in coding theory. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/33308

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


University of Texas – Austin

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

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 March 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 Mar 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 Mar 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

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

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 March 21, 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. 21 Mar 2019.

Vancouver:

Asteris M. Quadratic maximization under combinatorial constraints and related applications. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Mar 21]. 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

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

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 March 21, 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. 21 Mar 2019.

Vancouver:

Yi X. Learning with latent structures, robustness and non-linearity : non-convex approaches. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Mar 21]. 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

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

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 March 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 Mar 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 Mar 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


University of Texas – Austin

9. 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 March 21, 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. 21 Mar 2019.

Vancouver:

Park D. Efficient non-convex algorithms for large-scale learning problems. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Mar 21]. 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

10. -5988-8305. Populating a Linked Data Entity Name System.

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

 Resource Description Framework (RDF) is a graph-based data model used to publish data as a Web of Linked Data. RDF is an emergent foundation for… (more)

Subjects/Keywords: Resource Description Framework; Linked Data; Semantic Web; Instance matching; Entity resolution; Training set generation; Blocking; Property alignment; Domain-independence; Heterogeneity; MapReduce; Scalability

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

APA (6th Edition):

-5988-8305. (2016). Populating a Linked Data Entity Name System. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/39566

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):

-5988-8305. “Populating a Linked Data Entity Name System.” 2016. Thesis, University of Texas – Austin. Accessed March 21, 2019. http://hdl.handle.net/2152/39566.

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):

-5988-8305. “Populating a Linked Data Entity Name System.” 2016. Web. 21 Mar 2019.

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

Vancouver:

-5988-8305. Populating a Linked Data Entity Name System. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/2152/39566.

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:

-5988-8305. Populating a Linked Data Entity Name System. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/39566

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

.