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You searched for +publisher:"Cornell University" +contributor:("Sridharan, Karthik"). Showing records 1 – 11 of 11 total matches.

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Cornell University

1. Agarwal, Aman. Unbiased Learning-to-Rank from Logged Implicit Feedback.

Degree: PhD, Computer Science, 2020, Cornell University

 Learning-to-rank (LTR) search results in large scale industrial information retrieval settings, such as personal email and e-commerce, directly from logged implicit user feedback such as… (more)

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APA (6th Edition):

Agarwal, A. (2020). Unbiased Learning-to-Rank from Logged Implicit Feedback. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/70359

Chicago Manual of Style (16th Edition):

Agarwal, Aman. “Unbiased Learning-to-Rank from Logged Implicit Feedback.” 2020. Doctoral Dissertation, Cornell University. Accessed December 05, 2020. http://hdl.handle.net/1813/70359.

MLA Handbook (7th Edition):

Agarwal, Aman. “Unbiased Learning-to-Rank from Logged Implicit Feedback.” 2020. Web. 05 Dec 2020.

Vancouver:

Agarwal A. Unbiased Learning-to-Rank from Logged Implicit Feedback. [Internet] [Doctoral dissertation]. Cornell University; 2020. [cited 2020 Dec 05]. Available from: http://hdl.handle.net/1813/70359.

Council of Science Editors:

Agarwal A. Unbiased Learning-to-Rank from Logged Implicit Feedback. [Doctoral Dissertation]. Cornell University; 2020. Available from: http://hdl.handle.net/1813/70359


Cornell University

2. Guo, Chuan. Threats and Countermeasures in Machine Learning Applications.

Degree: PhD, Computer Science, 2020, Cornell University

 Machine learning as a technique of automatically constructing programs from past data for making future predictions has led to its adoption in many diverse areas… (more)

Subjects/Keywords: machine learning; privacy; security

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APA (6th Edition):

Guo, C. (2020). Threats and Countermeasures in Machine Learning Applications. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/70425

Chicago Manual of Style (16th Edition):

Guo, Chuan. “Threats and Countermeasures in Machine Learning Applications.” 2020. Doctoral Dissertation, Cornell University. Accessed December 05, 2020. http://hdl.handle.net/1813/70425.

MLA Handbook (7th Edition):

Guo, Chuan. “Threats and Countermeasures in Machine Learning Applications.” 2020. Web. 05 Dec 2020.

Vancouver:

Guo C. Threats and Countermeasures in Machine Learning Applications. [Internet] [Doctoral dissertation]. Cornell University; 2020. [cited 2020 Dec 05]. Available from: http://hdl.handle.net/1813/70425.

Council of Science Editors:

Guo C. Threats and Countermeasures in Machine Learning Applications. [Doctoral Dissertation]. Cornell University; 2020. Available from: http://hdl.handle.net/1813/70425


Cornell University

3. Lu, Xiaoyang. JOINT-PARAMETER ESTIMATION, BOOTSTRAP BIAS CORRECTION AND RISK FORECAST FOR EXTREME EVENTS.

Degree: PhD, Operations Research and Information Engineering, 2020, Cornell University

 Extreme value theory is a branch of mathematics that studies extreme events. Given a series modeling the event of interest, two quantities are of particular… (more)

Subjects/Keywords: bias reduction; bootstrap; Extreme value theory; random fields; Risk forecast; Tail estimation

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APA (6th Edition):

Lu, X. (2020). JOINT-PARAMETER ESTIMATION, BOOTSTRAP BIAS CORRECTION AND RISK FORECAST FOR EXTREME EVENTS. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/70412

Chicago Manual of Style (16th Edition):

Lu, Xiaoyang. “JOINT-PARAMETER ESTIMATION, BOOTSTRAP BIAS CORRECTION AND RISK FORECAST FOR EXTREME EVENTS.” 2020. Doctoral Dissertation, Cornell University. Accessed December 05, 2020. http://hdl.handle.net/1813/70412.

MLA Handbook (7th Edition):

Lu, Xiaoyang. “JOINT-PARAMETER ESTIMATION, BOOTSTRAP BIAS CORRECTION AND RISK FORECAST FOR EXTREME EVENTS.” 2020. Web. 05 Dec 2020.

Vancouver:

Lu X. JOINT-PARAMETER ESTIMATION, BOOTSTRAP BIAS CORRECTION AND RISK FORECAST FOR EXTREME EVENTS. [Internet] [Doctoral dissertation]. Cornell University; 2020. [cited 2020 Dec 05]. Available from: http://hdl.handle.net/1813/70412.

Council of Science Editors:

Lu X. JOINT-PARAMETER ESTIMATION, BOOTSTRAP BIAS CORRECTION AND RISK FORECAST FOR EXTREME EVENTS. [Doctoral Dissertation]. Cornell University; 2020. Available from: http://hdl.handle.net/1813/70412


Cornell University

4. Lykouris, Theodoros. Effective online decision-making in complex multi-agent systems.

Degree: PhD, Computer Science, 2019, Cornell University

 The emergence of online marketplaces has introduced important new dimensions to online decision-making. Classical algorithms developed to guarantee worst-case performance often focus strongly on the… (more)

Subjects/Keywords: Game theory; Operations research; bandits; Computer science; caching; regret; Online learning

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APA (6th Edition):

Lykouris, T. (2019). Effective online decision-making in complex multi-agent systems. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/67768

Chicago Manual of Style (16th Edition):

Lykouris, Theodoros. “Effective online decision-making in complex multi-agent systems.” 2019. Doctoral Dissertation, Cornell University. Accessed December 05, 2020. http://hdl.handle.net/1813/67768.

MLA Handbook (7th Edition):

Lykouris, Theodoros. “Effective online decision-making in complex multi-agent systems.” 2019. Web. 05 Dec 2020.

Vancouver:

Lykouris T. Effective online decision-making in complex multi-agent systems. [Internet] [Doctoral dissertation]. Cornell University; 2019. [cited 2020 Dec 05]. Available from: http://hdl.handle.net/1813/67768.

Council of Science Editors:

Lykouris T. Effective online decision-making in complex multi-agent systems. [Doctoral Dissertation]. Cornell University; 2019. Available from: http://hdl.handle.net/1813/67768


Cornell University

5. Gardner, Jacob. Discovering and Exploiting Structure for Gaussian Processes.

Degree: PhD, Computer Science, 2018, Cornell University

 Gaussian processes have emerged as a powerful tool for modeling complex and noisy functions. They have found wide applicability in personalized medicine, time series analysis,… (more)

Subjects/Keywords: Artificial intelligence

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APA (6th Edition):

Gardner, J. (2018). Discovering and Exploiting Structure for Gaussian Processes. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/59460

Chicago Manual of Style (16th Edition):

Gardner, Jacob. “Discovering and Exploiting Structure for Gaussian Processes.” 2018. Doctoral Dissertation, Cornell University. Accessed December 05, 2020. http://hdl.handle.net/1813/59460.

MLA Handbook (7th Edition):

Gardner, Jacob. “Discovering and Exploiting Structure for Gaussian Processes.” 2018. Web. 05 Dec 2020.

Vancouver:

Gardner J. Discovering and Exploiting Structure for Gaussian Processes. [Internet] [Doctoral dissertation]. Cornell University; 2018. [cited 2020 Dec 05]. Available from: http://hdl.handle.net/1813/59460.

Council of Science Editors:

Gardner J. Discovering and Exploiting Structure for Gaussian Processes. [Doctoral Dissertation]. Cornell University; 2018. Available from: http://hdl.handle.net/1813/59460


Cornell University

6. Knerr, Nathan. LOST IN DESIGN SPACE: INTERPRETING RELATIONS/STRUCTURE BETWEEN DECISIONS AND OBJECTIVES IN ENGINEERING DESIGN.

Degree: PhD, Mechanical Engineering, 2020, Cornell University

 In the early phase design of engineering systems, it has become increasingly popular to use a system model and an optimizer to generate a Pareto… (more)

Subjects/Keywords: Design Space Exploration; Design Statistics; Engineering Design; Multi-Criteria Decision Making; Multi-Objective Design Analytics; Visualization

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APA (6th Edition):

Knerr, N. (2020). LOST IN DESIGN SPACE: INTERPRETING RELATIONS/STRUCTURE BETWEEN DECISIONS AND OBJECTIVES IN ENGINEERING DESIGN. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/70440

Chicago Manual of Style (16th Edition):

Knerr, Nathan. “LOST IN DESIGN SPACE: INTERPRETING RELATIONS/STRUCTURE BETWEEN DECISIONS AND OBJECTIVES IN ENGINEERING DESIGN.” 2020. Doctoral Dissertation, Cornell University. Accessed December 05, 2020. http://hdl.handle.net/1813/70440.

MLA Handbook (7th Edition):

Knerr, Nathan. “LOST IN DESIGN SPACE: INTERPRETING RELATIONS/STRUCTURE BETWEEN DECISIONS AND OBJECTIVES IN ENGINEERING DESIGN.” 2020. Web. 05 Dec 2020.

Vancouver:

Knerr N. LOST IN DESIGN SPACE: INTERPRETING RELATIONS/STRUCTURE BETWEEN DECISIONS AND OBJECTIVES IN ENGINEERING DESIGN. [Internet] [Doctoral dissertation]. Cornell University; 2020. [cited 2020 Dec 05]. Available from: http://hdl.handle.net/1813/70440.

Council of Science Editors:

Knerr N. LOST IN DESIGN SPACE: INTERPRETING RELATIONS/STRUCTURE BETWEEN DECISIONS AND OBJECTIVES IN ENGINEERING DESIGN. [Doctoral Dissertation]. Cornell University; 2020. Available from: http://hdl.handle.net/1813/70440


Cornell University

7. Kang, Keegan. Data Dependent Random Projections.

Degree: PhD, Statistics, 2017, Cornell University

 Random projections is a technique used primarily in dimension reduction, in order to estimate distances in data. They can be thought of a linear transformation… (more)

Subjects/Keywords: information retrieval; random projections; Computer science; Statistics; control variates

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APA (6th Edition):

Kang, K. (2017). Data Dependent Random Projections. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/51575

Chicago Manual of Style (16th Edition):

Kang, Keegan. “Data Dependent Random Projections.” 2017. Doctoral Dissertation, Cornell University. Accessed December 05, 2020. http://hdl.handle.net/1813/51575.

MLA Handbook (7th Edition):

Kang, Keegan. “Data Dependent Random Projections.” 2017. Web. 05 Dec 2020.

Vancouver:

Kang K. Data Dependent Random Projections. [Internet] [Doctoral dissertation]. Cornell University; 2017. [cited 2020 Dec 05]. Available from: http://hdl.handle.net/1813/51575.

Council of Science Editors:

Kang K. Data Dependent Random Projections. [Doctoral Dissertation]. Cornell University; 2017. Available from: http://hdl.handle.net/1813/51575


Cornell University

8. Shi, Jonathan. Tensor rank decompositions via the pseudo-moment method.

Degree: PhD, Computer Science, 2019, Cornell University

 Over a series of four articles and an introduction, a "method of pseudo-moments" is developed which gives polynomial-time tensor rank decompositions for a variety of… (more)

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APA (6th Edition):

Shi, J. (2019). Tensor rank decompositions via the pseudo-moment method. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/69989

Chicago Manual of Style (16th Edition):

Shi, Jonathan. “Tensor rank decompositions via the pseudo-moment method.” 2019. Doctoral Dissertation, Cornell University. Accessed December 05, 2020. http://hdl.handle.net/1813/69989.

MLA Handbook (7th Edition):

Shi, Jonathan. “Tensor rank decompositions via the pseudo-moment method.” 2019. Web. 05 Dec 2020.

Vancouver:

Shi J. Tensor rank decompositions via the pseudo-moment method. [Internet] [Doctoral dissertation]. Cornell University; 2019. [cited 2020 Dec 05]. Available from: http://hdl.handle.net/1813/69989.

Council of Science Editors:

Shi J. Tensor rank decompositions via the pseudo-moment method. [Doctoral Dissertation]. Cornell University; 2019. Available from: http://hdl.handle.net/1813/69989

9. Wallingford, Matthew C. Lowshot Segmentation.

Degree: M.S., Computer Science, Computer Science, 2019, Cornell University

 Learning from limited supervision has become an area of interest in machine learning because deep learning systems have demonstrated a dependence on large, labeled data… (more)

Subjects/Keywords: Lowshot; Semantic Segmentation; computer vision; Computer science; machine learning; Fewshot; Heavy-tailed Distribution

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APA (6th Edition):

Wallingford, M. C. (2019). Lowshot Segmentation. (Masters Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/67404

Chicago Manual of Style (16th Edition):

Wallingford, Matthew C. “Lowshot Segmentation.” 2019. Masters Thesis, Cornell University. Accessed December 05, 2020. http://hdl.handle.net/1813/67404.

MLA Handbook (7th Edition):

Wallingford, Matthew C. “Lowshot Segmentation.” 2019. Web. 05 Dec 2020.

Vancouver:

Wallingford MC. Lowshot Segmentation. [Internet] [Masters thesis]. Cornell University; 2019. [cited 2020 Dec 05]. Available from: http://hdl.handle.net/1813/67404.

Council of Science Editors:

Wallingford MC. Lowshot Segmentation. [Masters Thesis]. Cornell University; 2019. Available from: http://hdl.handle.net/1813/67404

10. Niculae, Vlad. Learning Deep Models with Linguistically-Inspired Structure.

Degree: PhD, Computer Science, 2018, Cornell University

 Many applied machine learning tasks involve structured representations. This is particularly the case in natural language processing (NLP), where the discrete, compositional nature of words… (more)

Subjects/Keywords: Computer science; ML; NLP; SparseMAP; sparsity; structure

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APA (6th Edition):

Niculae, V. (2018). Learning Deep Models with Linguistically-Inspired Structure. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/59540

Chicago Manual of Style (16th Edition):

Niculae, Vlad. “Learning Deep Models with Linguistically-Inspired Structure.” 2018. Doctoral Dissertation, Cornell University. Accessed December 05, 2020. http://hdl.handle.net/1813/59540.

MLA Handbook (7th Edition):

Niculae, Vlad. “Learning Deep Models with Linguistically-Inspired Structure.” 2018. Web. 05 Dec 2020.

Vancouver:

Niculae V. Learning Deep Models with Linguistically-Inspired Structure. [Internet] [Doctoral dissertation]. Cornell University; 2018. [cited 2020 Dec 05]. Available from: http://hdl.handle.net/1813/59540.

Council of Science Editors:

Niculae V. Learning Deep Models with Linguistically-Inspired Structure. [Doctoral Dissertation]. Cornell University; 2018. Available from: http://hdl.handle.net/1813/59540

11. Foster, Dylan James. Adaptive Learning: Algorithms and Complexity.

Degree: PhD, Computer Science, 2019, Cornell University

 Recent empirical success in machine learning has led to major breakthroughs in application domains including computer vision, robotics, and natural language processing. There is a… (more)

Subjects/Keywords: Statistics; adaptivity; bandits; statistical learning; Optimization; Computer science; Online learning; machine learning

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APA (6th Edition):

Foster, D. J. (2019). Adaptive Learning: Algorithms and Complexity. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/67219

Chicago Manual of Style (16th Edition):

Foster, Dylan James. “Adaptive Learning: Algorithms and Complexity.” 2019. Doctoral Dissertation, Cornell University. Accessed December 05, 2020. http://hdl.handle.net/1813/67219.

MLA Handbook (7th Edition):

Foster, Dylan James. “Adaptive Learning: Algorithms and Complexity.” 2019. Web. 05 Dec 2020.

Vancouver:

Foster DJ. Adaptive Learning: Algorithms and Complexity. [Internet] [Doctoral dissertation]. Cornell University; 2019. [cited 2020 Dec 05]. Available from: http://hdl.handle.net/1813/67219.

Council of Science Editors:

Foster DJ. Adaptive Learning: Algorithms and Complexity. [Doctoral Dissertation]. Cornell University; 2019. Available from: http://hdl.handle.net/1813/67219

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