I am a master’s student in the Department of Computer Science at the University of British Columbia, where I am advised by Mark Schmidt. My research interests are in optimization for machine learning.

Recent Activities






    1. To Each Optimizer a Norm, To Each Norm its Generalization. S. Vaswani, R. Babanezhad, J. Gallego, A. Mishkin, S. Lacoste-Julien, N. Le Roux. arXiv Preprint, 2020.

    2. Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates. S. Vaswani, A. Mishkin, I. Laradji, M. Schmidt, G. Gidel, S. Lacoste-Julien. NeurIPS, 2019.

    3. SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. A. Mishkin, F. Kunstner, D. Nielsen, M. Schmidt, M. E. Khan. NeurIPS, 2018.

    4. Web ValueCharts: Analyzing Individual and Group Preferences with Interactive, Web-based Visualizations. A. Mishkin. Review of Undergraduate Computer Science, 2018.


    1. Talks about Painless SGD:
    2. Talks at the UBC Machine Learning Reading Group (MLRG):
    3. CUCSC 2017: Web ValueCharts: Exploring Individual and Group Preferences Through Interactive Web-based Visualizations.
    4. MURC 2017: Web ValueCharts: Supporting Decision Makers with Interactive, Web-Based Visualizations


    I can be contacted at amishkin@cs.ubc.ca and #MishkinAaron. My GitHub is aaronpmishkin.

    Aaron Mishkin