Mark Schmidt

Associate Professor
Email: schmidtm [at] cs [dot] ubc [dot] ca
Office: ICCS 193
Phone: 604-822-6421 (I usually don't answer and don't check messages)

Curriculum Vitae

Assistant Professor, University of British Columbia (2014-Present)
Postdoc, Simon Fraser University (2013-2014)
Postdoc, Ecole Normale Superieure (2011-2013)
Postdoc, University of British Columbia (2010)
Ph.D., University of British Columbia (2005-2010)
M.Sc., University of Alberta (2003-2005)
B.Sc., University of Alberta (2000-2003)


- Machine learning
- Numerical Optimizaiton
- Probabilistic Graphical Models
- Causality

Selected Publications

Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields.
M. Schmidt, R. Babanezhad, M.O. Ahmed, A. Defazio, A. Clifton, A. Sarkar. International Conference on Artitifical Intelligence and Statistics, 2015.

Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics.
V. Cevher, S. Becker. M. Schmidt. IEEE Signal Processing Magazine, 2014.

Block-Coordinate Frank-Wolfe Optimization for Structural SVMs.
S. Lacoste-Julien, M. Jaggi, M. Schmidt, P. Pletscher. International Conference on Machine Learning, 2013.

A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets.
N. Le Roux, M. Schmidt, F. Bach. Advances in Neural Information Processing Systems, 2012.

Hybrid Deterministic-Stochastic Methods for Data Fitting.
M. Friedlander, M. Schmidt. SIAM Journal of Scientific Computing, 2012.

Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization.
M. Schmidt, N. Le Roux, F. Bach. Advances in Neural Information Processing Systems, 2011.

Projected Newton-type Methods in Machine Learning.
M. Schmidt, D. Kim, S. Sra. Optimization for Machine Learning (S. Sra, S. Nowozin, S.Wright), MIT Press 2011.

Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials.
M. Schmidt, K. Murphy. International Conference on Artificial Intelligence and Statistics, 2010.

Causal Learning without DAGs.
D. Duvenaud, D. Eaton, K. Murphy, M. Schmidt. Journal of Machine Learning Research Workshop and Conference Proceedings, 2010.

Group Sparse Priors for Covariance Estimation.
B. Marlin, M. Schmidt, K. Murphy. Conference on Uncertainty in Artificial Intelligence, 2009.

Latest CS Courses

2019 Winter

CPSC 532M  –  Topics in Artificial Intelligence
CPSC 540  –  Machine Learning
CPSC 340  –  Machine Learning and Data Mining
CPSC 340  –  Machine Learning and Data Mining

2018 Winter

CPSC 340  –  Machine Learning and Data Mining

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Tel: 604-822-3061 | Fax: 604-822-5485
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