1. Sparsity-based generalization bounds for predictive sparse coding

  2. Nishant Mehta and Alexander Gray

  3. ICML 2013

  4.   Long version (submitted to JMLR): On the sample complexity of predictive sparse coding

  5. arXiv 1202.4050, 2012

  6. MLPACK: A scalable C++ machine learning library

  7. Ryan R. Curtin, James R. Cline, N.P. Slagle, William B. March, Parikshit Ram, Nishant Mehta, and Alexander Gray.

  8. JMLR 2013

  9. Minimax multi-task learning and a generalized loss-compositional paradigm for MTL

  10. Nishant Mehta, Dongryeol Lee, and Alexander Gray

  11. NIPS 2012

  12.   Shorter, workshop version: Minimax multi-task learning

  13. NIPS 2012 Workshop on Multi-Trade-offs in Machine Learning

  14. Computer detection approaches for the identification of phasic electromyographic (EMG) activity during human sleep

  15. Jacqueline Fairley, George Georgoulas, Nishant Mehta, Alexander Gray, and Donald Bliwise

  16. Biomedical Signal Processing and Control, 2012

  17. Discriminative sparse coding for classification and regression

  18. Nishant Mehta and Alexander Gray

  19. The Learning Workshop (Snowbird), 2011. Oral presentation

  20. Generative and latent mean map kernels

  21. Nishant Mehta and Alexander Gray

  22. arXiv 1005.0188, 2010

  23. Recognizing sign language from brain imaging

  24. Nishant Mehta, Thad Starner, Melody Moore Jackson, Karolyn Babalola, and George Andrew James

  25. International Conference on Pattern Recognition (ICPR), 2010

  26.   Longer, earlier version: Recognizing sign language from brain imaging

  27. GVU Technical Report GIT-GVU-09-06, 2009

  28. Optimal control strategies for an SSVEP-based brain-computer interface

  29. Nishant Mehta, Sadhir Hussain, and Melody Moore Jackson

  30. International Journal of Human-Computer Interaction, 2010

  31. FuncICA for time series pattern discovery

  32. Nishant Mehta and Alexander Gray

  33. SIAM Data Mining, 2009. Nominated for best paper award. Selected for oral presentation

  34. Estimating neural signal dependence using kernels

  35. Nishant Mehta, Alexander Gray, Thad Starner, and Melody Moore Jackson

  36. NIPS 2008 Workshop on Statistical Analysis and Modeling of Response Dependencies in Neural Populations

I am now at Australian National University. You should be redirected.
I’ll be starting as a Research Fellow at Australian National University’s Research School of Computer Science, luckily working with Bob Williamson on reconceiving machine learning. I recently completed my PhD in Computer science, studying machine learning in Georgia Tech’s College of Computing, under my thesis advisor Alexander Gray (FASTlab).


At Georgia Tech, my research focused on sparse representations, multi-task learning, and learning latent representations using supervised information, with an emphasis on learning theoretic results. My dissertation, “On sparse representations and new meta-learning paradigms for representation learning,” established generalization error bounds for learning sparse representations for supervised tasks and also introduced new multi-task learning and meta-learning frameworks.


Other things I am interested in include meta-learning, new learning frameworks, representation learning, kernel methods, manifolds, information geometry, obtaining deeper understandings of fundamental objects in learning, and cool proof techniques.


Papers

Teaching

I have been a teaching assistant for

  1. Advanced Machine Learning (Alexander Gray’s “Computational Data Analysis”)

  2. Machine Learning (Guy Lebanon’s “Computational Data Analysis)

  3. Discrete Algorithms (Alberto Apostolico’s “Computational Science & Engineering Algorithms”)

Nishant Mehta


niche ατ |c|c.ga|tech.ed|u

somewhere in Canberra

            cv

Relevant Coursework

Machine Learning and Statistics

  1. Selected topics in high dimensional probability and statistics,

  2. Statistical estimation, Machine learning theory, Probabilistic graphical models,

  3. Natural language understanding, Combinatorial methods in density estimation,

  4. Introduction to information theory, Neural coding, Spatial statistics

Algorithms

  1. Design and analysis of algorithms, Spectral algorithms, Bioinformatics algorithms (String algorithms)

Math

  1. Introduction to geometry and topology I and II, Introduction to graph theory

Optimization

  1. Linear optimization, Nonlinear optimization