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You searched for +publisher:"Georgia Tech" +contributor:("Balcan, Maria-Florina"). Showing records 1 – 16 of 16 total matches.

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1. Balasubramanian, Krishnakumar. Learning matrix and functional models in high-dimensions.

Degree: PhD, Computer Science, 2014, Georgia Tech

 Statistical machine learning methods provide us with a principled framework for extracting meaningful information from noisy high-dimensional data sets. A significant feature of such procedures… (more)

Subjects/Keywords: Statistics; Machine learning; Matrix; Kernel; Consistency

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APA (6th Edition):

Balasubramanian, K. (2014). Learning matrix and functional models in high-dimensions. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/52284

Chicago Manual of Style (16th Edition):

Balasubramanian, Krishnakumar. “Learning matrix and functional models in high-dimensions.” 2014. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/52284.

MLA Handbook (7th Edition):

Balasubramanian, Krishnakumar. “Learning matrix and functional models in high-dimensions.” 2014. Web. 18 Apr 2021.

Vancouver:

Balasubramanian K. Learning matrix and functional models in high-dimensions. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/52284.

Council of Science Editors:

Balasubramanian K. Learning matrix and functional models in high-dimensions. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/52284

2. Ganti Mahapatruni, Ravi Sastry. New formulations for active learning.

Degree: PhD, Computer Science, 2014, Georgia Tech

 In this thesis, we provide computationally efficient algorithms with provable statistical guarantees, for the problem of active learning, by using ideas from sequential analysis. We… (more)

Subjects/Keywords: Active learning; Sequential analysis; Stochastic optimization; Active learning; Algorithms; Sequential analysis; Mathematical optimization; Machine learning

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APA (6th Edition):

Ganti Mahapatruni, R. S. (2014). New formulations for active learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/51801

Chicago Manual of Style (16th Edition):

Ganti Mahapatruni, Ravi Sastry. “New formulations for active learning.” 2014. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/51801.

MLA Handbook (7th Edition):

Ganti Mahapatruni, Ravi Sastry. “New formulations for active learning.” 2014. Web. 18 Apr 2021.

Vancouver:

Ganti Mahapatruni RS. New formulations for active learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/51801.

Council of Science Editors:

Ganti Mahapatruni RS. New formulations for active learning. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/51801

3. He, Niao. Saddle point techniques in convex composite and error-in-measurement optimization.

Degree: PhD, Industrial and Systems Engineering, 2015, Georgia Tech

 This dissertation aims to develop efficient algorithms with improved scalability and stability properties for large-scale optimization and optimization under uncertainty, and to bridge some of… (more)

Subjects/Keywords: Nonsmooth optimization; Composite minimization; First order methods; Stochastic optimization; Mirror prox

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APA (6th Edition):

He, N. (2015). Saddle point techniques in convex composite and error-in-measurement optimization. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54400

Chicago Manual of Style (16th Edition):

He, Niao. “Saddle point techniques in convex composite and error-in-measurement optimization.” 2015. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/54400.

MLA Handbook (7th Edition):

He, Niao. “Saddle point techniques in convex composite and error-in-measurement optimization.” 2015. Web. 18 Apr 2021.

Vancouver:

He N. Saddle point techniques in convex composite and error-in-measurement optimization. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/54400.

Council of Science Editors:

He N. Saddle point techniques in convex composite and error-in-measurement optimization. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/54400


Georgia Tech

4. Li, Yaxian. Lower bounds for integer programming problems.

Degree: PhD, Industrial and Systems Engineering, 2013, Georgia Tech

 Solving real world problems with mixed integer programming (MIP) involves efforts in modeling and efficient algorithms. To solve a minimization MIP problem, a lower bound… (more)

Subjects/Keywords: Lower bounds; Integer programming problems; Multi-dimensional knapsack problem; Algorithms; Knapsack problem (Mathematics); Integer programming

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APA (6th Edition):

Li, Y. (2013). Lower bounds for integer programming problems. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/48959

Chicago Manual of Style (16th Edition):

Li, Yaxian. “Lower bounds for integer programming problems.” 2013. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/48959.

MLA Handbook (7th Edition):

Li, Yaxian. “Lower bounds for integer programming problems.” 2013. Web. 18 Apr 2021.

Vancouver:

Li Y. Lower bounds for integer programming problems. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/48959.

Council of Science Editors:

Li Y. Lower bounds for integer programming problems. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/48959


Georgia Tech

5. Mehta, Nishant A. On sparse representations and new meta-learning paradigms for representation learning.

Degree: PhD, Computer Science, 2013, Georgia Tech

 Given the "right" representation, learning is easy. This thesis studies representation learning and meta-learning, with a special focus on sparse representations. Meta-learning is fundamental to… (more)

Subjects/Keywords: Learning theory; Data-dependent complexity; Luckiness; Dictionary learning; Sparse coding; Lasso; Multi-task learning; Meta-learning; Learning to learn

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APA (6th Edition):

Mehta, N. A. (2013). On sparse representations and new meta-learning paradigms for representation learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/52159

Chicago Manual of Style (16th Edition):

Mehta, Nishant A. “On sparse representations and new meta-learning paradigms for representation learning.” 2013. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/52159.

MLA Handbook (7th Edition):

Mehta, Nishant A. “On sparse representations and new meta-learning paradigms for representation learning.” 2013. Web. 18 Apr 2021.

Vancouver:

Mehta NA. On sparse representations and new meta-learning paradigms for representation learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/52159.

Council of Science Editors:

Mehta NA. On sparse representations and new meta-learning paradigms for representation learning. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/52159


Georgia Tech

6. Berlind, Christopher. New insights on the power of active learning.

Degree: PhD, Computer Science, 2015, Georgia Tech

 Traditional supervised machine learning algorithms are expected to have access to a large corpus of labeled examples, but the massive amount of data available in… (more)

Subjects/Keywords: Machine learning; Learning theory; Active learning; Semi-supervised learning; Domain adaptation; Large margin learning

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APA (6th Edition):

Berlind, C. (2015). New insights on the power of active learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53948

Chicago Manual of Style (16th Edition):

Berlind, Christopher. “New insights on the power of active learning.” 2015. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/53948.

MLA Handbook (7th Edition):

Berlind, Christopher. “New insights on the power of active learning.” 2015. Web. 18 Apr 2021.

Vancouver:

Berlind C. New insights on the power of active learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/53948.

Council of Science Editors:

Berlind C. New insights on the power of active learning. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53948

7. Gui, Luyi. Managing and optimizing decentralized networks with resource sharing.

Degree: PhD, Industrial and Systems Engineering, 2013, Georgia Tech

 Resource sharing is a common collaborative strategy used in practice. It has the potential to create synergistic value and leads to higher system efficiency. However,… (more)

Subjects/Keywords: Legislation; Mechanism design; Resource sharing; Decentralized networks; Optimization; EPR; Game theory; Cooperative games (Mathematics); Self-interest; Mathematical optimization

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APA (6th Edition):

Gui, L. (2013). Managing and optimizing decentralized networks with resource sharing. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/47707

Chicago Manual of Style (16th Edition):

Gui, Luyi. “Managing and optimizing decentralized networks with resource sharing.” 2013. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/47707.

MLA Handbook (7th Edition):

Gui, Luyi. “Managing and optimizing decentralized networks with resource sharing.” 2013. Web. 18 Apr 2021.

Vancouver:

Gui L. Managing and optimizing decentralized networks with resource sharing. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/47707.

Council of Science Editors:

Gui L. Managing and optimizing decentralized networks with resource sharing. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/47707

8. Minsker, Stanislav. Non-asymptotic bounds for prediction problems and density estimation.

Degree: PhD, Mathematics, 2012, Georgia Tech

 This dissertation investigates the learning scenarios where a high-dimensional parameter has to be estimated from a given sample of fixed size, often smaller than the… (more)

Subjects/Keywords: Active learning; Sparse recovery; Oracle inequality; Confidence bands; Infinite dictionary; Estimation theory Asymptotic theory; Estimation theory; Distribution (Probability theory); Prediction theory; Active learning; Algorithms; Mathematical optimization; Chebyshev approximation

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APA (6th Edition):

Minsker, S. (2012). Non-asymptotic bounds for prediction problems and density estimation. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/44808

Chicago Manual of Style (16th Edition):

Minsker, Stanislav. “Non-asymptotic bounds for prediction problems and density estimation.” 2012. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/44808.

MLA Handbook (7th Edition):

Minsker, Stanislav. “Non-asymptotic bounds for prediction problems and density estimation.” 2012. Web. 18 Apr 2021.

Vancouver:

Minsker S. Non-asymptotic bounds for prediction problems and density estimation. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/44808.

Council of Science Editors:

Minsker S. Non-asymptotic bounds for prediction problems and density estimation. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/44808

9. Dudebout, Nicolas. Empirical-evidence equilibria in stochastic games.

Degree: PhD, Electrical and Computer Engineering, 2014, Georgia Tech

 The objective of this research is to develop the framework of empirical-evidence equilibria (EEEs) in stochastic games. This framework was developed while attempting to design… (more)

Subjects/Keywords: Equilibrium; Stochastic games; Bounded rationality

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APA (6th Edition):

Dudebout, N. (2014). Empirical-evidence equilibria in stochastic games. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/52205

Chicago Manual of Style (16th Edition):

Dudebout, Nicolas. “Empirical-evidence equilibria in stochastic games.” 2014. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/52205.

MLA Handbook (7th Edition):

Dudebout, Nicolas. “Empirical-evidence equilibria in stochastic games.” 2014. Web. 18 Apr 2021.

Vancouver:

Dudebout N. Empirical-evidence equilibria in stochastic games. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/52205.

Council of Science Editors:

Dudebout N. Empirical-evidence equilibria in stochastic games. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/52205

10. Xiao, Ying. New tools for unsupervised learning.

Degree: PhD, Computer Science, 2014, Georgia Tech

 In an unsupervised learning problem, one is given an unlabelled dataset and hopes to find some hidden structure; the prototypical example is clustering similar data.… (more)

Subjects/Keywords: Tensor; Spectral decomposition; Unsupervised learning; Independent component analysis; Fourier transform; Gaussian mixture model; Feature selection

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APA (6th Edition):

Xiao, Y. (2014). New tools for unsupervised learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/52995

Chicago Manual of Style (16th Edition):

Xiao, Ying. “New tools for unsupervised learning.” 2014. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/52995.

MLA Handbook (7th Edition):

Xiao, Ying. “New tools for unsupervised learning.” 2014. Web. 18 Apr 2021.

Vancouver:

Xiao Y. New tools for unsupervised learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/52995.

Council of Science Editors:

Xiao Y. New tools for unsupervised learning. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/52995

11. Ram, Parikshit. New paradigms for approximate nearest-neighbor search.

Degree: PhD, Computational Science and Engineering, 2013, Georgia Tech

 Nearest-neighbor search is a very natural and universal problem in computer science. Often times, the problem size necessitates approximation. In this thesis, I present new… (more)

Subjects/Keywords: Similarity search; Nearest-neighbor search; Computational geometry; Algorithms and analysis; Nearest neighbor analysis (Statistics); Approximation algorithms; Search theory

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APA (6th Edition):

Ram, P. (2013). New paradigms for approximate nearest-neighbor search. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/49112

Chicago Manual of Style (16th Edition):

Ram, Parikshit. “New paradigms for approximate nearest-neighbor search.” 2013. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/49112.

MLA Handbook (7th Edition):

Ram, Parikshit. “New paradigms for approximate nearest-neighbor search.” 2013. Web. 18 Apr 2021.

Vancouver:

Ram P. New paradigms for approximate nearest-neighbor search. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/49112.

Council of Science Editors:

Ram P. New paradigms for approximate nearest-neighbor search. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/49112

12. Mac Dermed, Liam Charles. Value methods for efficiently solving stochastic games of complete and incomplete information.

Degree: PhD, Computer Science, 2013, Georgia Tech

 Multi-agent reinforcement learning (MARL) poses the same planning problem as traditional reinforcement learning (RL): What actions over time should an agent take in order to… (more)

Subjects/Keywords: Multi-agent planning; Game theory; Reinforcement learning; Reinforcement learning; Multiagent systems; Game theory

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APA (6th Edition):

Mac Dermed, L. C. (2013). Value methods for efficiently solving stochastic games of complete and incomplete information. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/50270

Chicago Manual of Style (16th Edition):

Mac Dermed, Liam Charles. “Value methods for efficiently solving stochastic games of complete and incomplete information.” 2013. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/50270.

MLA Handbook (7th Edition):

Mac Dermed, Liam Charles. “Value methods for efficiently solving stochastic games of complete and incomplete information.” 2013. Web. 18 Apr 2021.

Vancouver:

Mac Dermed LC. Value methods for efficiently solving stochastic games of complete and incomplete information. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/50270.

Council of Science Editors:

Mac Dermed LC. Value methods for efficiently solving stochastic games of complete and incomplete information. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/50270


Georgia Tech

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

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

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Chen, Shang-Tse. “AI-infused security: Robust defense by bridging theory and practice.” 2019. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/62296.

MLA Handbook (7th Edition):

Chen, Shang-Tse. “AI-infused security: Robust defense by bridging theory and practice.” 2019. Web. 18 Apr 2021.

Vancouver:

Chen S. AI-infused security: Robust defense by bridging theory and practice. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/62296.

Council of Science Editors:

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


Georgia Tech

14. Liang, Yingyu. Modern aspects of unsupervised learning.

Degree: PhD, Computer Science, 2014, Georgia Tech

 Unsupervised learning has become more and more important due to the recent explosion of data. Clustering, a key topic in unsupervised learning, is a well-studied… (more)

Subjects/Keywords: Unsupervised learning; Clustering; Perturbation resilience; Distributed clustering; Community detection

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APA (6th Edition):

Liang, Y. (2014). Modern aspects of unsupervised learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/52282

Chicago Manual of Style (16th Edition):

Liang, Yingyu. “Modern aspects of unsupervised learning.” 2014. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/52282.

MLA Handbook (7th Edition):

Liang, Yingyu. “Modern aspects of unsupervised learning.” 2014. Web. 18 Apr 2021.

Vancouver:

Liang Y. Modern aspects of unsupervised learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/52282.

Council of Science Editors:

Liang Y. Modern aspects of unsupervised learning. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/52282

15. Balavoine, Aurele. Mathematical analysis of a dynamical system for sparse recovery.

Degree: PhD, Electrical and Computer Engineering, 2014, Georgia Tech

 This thesis presents the mathematical analysis of a continuous-times system for sparse signal recovery. Sparse recovery arises in Compressed Sensing (CS), where signals of large… (more)

Subjects/Keywords: Sparse recovery; Neural network; L1-minimization; Nonsmooth optimization; Compressed sensing; Tracking; ISTA; LCA; Sparse matrices; Signal processing Digital techniques; Mathematical optimization

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APA (6th Edition):

Balavoine, A. (2014). Mathematical analysis of a dynamical system for sparse recovery. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/51882

Chicago Manual of Style (16th Edition):

Balavoine, Aurele. “Mathematical analysis of a dynamical system for sparse recovery.” 2014. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/51882.

MLA Handbook (7th Edition):

Balavoine, Aurele. “Mathematical analysis of a dynamical system for sparse recovery.” 2014. Web. 18 Apr 2021.

Vancouver:

Balavoine A. Mathematical analysis of a dynamical system for sparse recovery. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/51882.

Council of Science Editors:

Balavoine A. Mathematical analysis of a dynamical system for sparse recovery. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/51882


Georgia Tech

16. Karande, Chinmay. Algorithms and mechanism design for multi-agent systems.

Degree: PhD, Computing, 2010, Georgia Tech

 A scenario where multiple entities interact with a common environment to achieve individual and common goals either co-operatively or competitively can be classified as a… (more)

Subjects/Keywords: Multi-agent systems; Online auction; Algorithms; Mechanism design; Submodular functions; Matroids

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APA (6th Edition):

Karande, C. (2010). Algorithms and mechanism design for multi-agent systems. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/37229

Chicago Manual of Style (16th Edition):

Karande, Chinmay. “Algorithms and mechanism design for multi-agent systems.” 2010. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/37229.

MLA Handbook (7th Edition):

Karande, Chinmay. “Algorithms and mechanism design for multi-agent systems.” 2010. Web. 18 Apr 2021.

Vancouver:

Karande C. Algorithms and mechanism design for multi-agent systems. [Internet] [Doctoral dissertation]. Georgia Tech; 2010. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/37229.

Council of Science Editors:

Karande C. Algorithms and mechanism design for multi-agent systems. [Doctoral Dissertation]. Georgia Tech; 2010. Available from: http://hdl.handle.net/1853/37229

.