Nishant Mehta
Assistant Professor, Computer Science
University of Victoria
office: ECS 608 (Note: I don't check voicemail!)
n<lastname>@uvic.ca                                        CV
**Prospective Students**                                                 Machine Learning Reading Group
Bio
I work on the theoretical side of machine learning, including statistical learning theory, online learning / sequential prediction, and developing theoretical foundations for representation learning.

Prior to coming to Victoria, I previously was a postdoc at CWI in Amsterdam (with Peter Grünwald) and at the Australian National University in Canberra (with Bob Williamson). I did my PhD in the College of Computing at Georgia Tech, where I was advised by Alex Gray.

Papers

also see my Google Scholar profile

Problem-dependent regret bounds for online learning with feedback graphs
Bingshan Hu, Nishant Mehta, Jianping Pan
Accepted to UAI 2019

Intelligent caching algorithms in heterogeneous wireless networks with uncertainty
Bingshan Hu, Yunjin Chen, Zhiming Huang, Nishant Mehta, Jianping Pan
Accepted to IEEE International Conference on Distributed Computing Systems (ICDCS) 2019

Multi-observation regression
Rafael Frongillo, Nishant Mehta, Tom Morgan, and Bo Waggoner
AISTATS 2019

A tight excess risk bound via a unified PAC-Bayesian-Rademacher-Shtarkov-MDL complexity
Peter Grünwald and Nishant Mehta
ALT 2019 - slides
long version

Interpreting word-level hidden state behaviour of character-level LSTM language models
Avery Hiebert, Cole Peterson, Alona Fyshe, and Nishant Mehta
EMNLP 2018 Workshop on Analyzing and Interpreting Neural Networks for NLP, 2018

Fast rates with high probability in exp-concave statistical learning
Nishant Mehta
AISTATS 2017

Fast rates with unbounded losses
Peter Grünwald and Nishant Mehta
arXiv 1605.00252, 2016

Fast rates in statistical and online learning
Tim van Erven, Peter Grünwald, Nishant Mehta, Mark Reid, and Robert Williamson
JMLR, 2015 (Special issue in memory of Alexey Chervonenkis)

Generalized mixability via entropic duality
Mark Reid, Rafael Frongillo, Robert Williamson, and Nishant Mehta
COLT, 2015

From stochastic mixability to fast rates
Nishant Mehta and Robert Williamson
NIPS, 2014 (full oral presentation) - slides
long version

Sparsity-based generalization bounds for predictive sparse coding
Nishant Mehta and Alexander Gray
ICML, 2013
long version

MLPACK: A scalable C++ machine learning library
Ryan Curtin, James Cline, N.P. Slagle, William March, Parikshit Ram, Nishant Mehta, and Alexander Gray
JMLR, 2013

Minimax multi-task learning and a generalized loss-compositional paradigm for MTL
Nishant Mehta, Dongryeol Lee, and Alexander Gray
NIPS, 2012
version at NIPS 2012 Workshop on Multi-Trade-offs in Machine Learning

Computer detection approaches for the identification of phasic electromyographic (EMG) activity during human sleep
Jacqueline Fairley, George Georgoulas, Nishant Mehta, Alexander Gray, and Donald Bliwise
Biomedical Signal Processing and Control, 2012

Discriminative sparse coding for classification and regression
Nishant Mehta and Alexander Gray
The Learning Workshop (Snowbird), 2011 (oral presentation)

Generative and latent mean map kernels
Nishant Mehta and Alexander Gray
arXiv 1005.0188, 2010

Recognizing sign language from brain imaging
Nishant Mehta, Thad Starner, Melody Moore Jackson, Karolyn Babalola, and George Andrew James
International Conference on Pattern Recognition (ICPR), 2010
GVU Technical Report GIT-GVU-09-06, 2009

Optimal control strategies for an SSVEP-based brain-computer interface
Nishant Mehta, Sadhir Hussain, and Melody Moore Jackson
International Journal of Human-Computer Interaction, 2010

FuncICA for time series pattern discovery
Nishant Mehta and Alexander Gray
SIAM Data Mining, 2009 (nominated for best paper award; selected for oral presentation)
slides

Estimating neural signal dependence using kernels
Nishant Mehta, Alexander Gray, Thad Starner, and Melody Moore Jackson
NIPS 2008 Workshop on Statistical Analysis and Modeling of Response Dependencies in Neural Populations

Students
Bingshan Hu, PhD student (Co-advised with Jianping Pan)
Sajjad Azami, MSc student
Sharoff Pon Kumar, MSc student
Hamid Shayestehmanesh, MSc student
Avery Hiebert, Undergrad
Teaching
Machine Learning Theory (CSC 482A/581A), Spring 2019. course webpage
Algorithms and Data Structures (CSC 226), Fall 2018.
Machine Learning Theory (CSC 482A/581A), Spring 2018.
Algorithms and Data Structures (CSC 226), Fall 2017.
Information-Theoretic Learning (Leiden University, Spring 2016). Together with Peter Grünwald.
Miscellanea
Software
MLPACK