Publications by Mark Schmidt

2013

• Block-Coordinate Frank-Wolfe Optimization for Structural SVMs.
  S. Lacoste-Julien, M. Jaggi, M. Schmidt, P. Pletscher. ICML, 2013. [pdf] [poster] [slides] [short version]

2012

• A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets.
  N. Le Roux, M. Schmidt, F. Bach. NIPS, 2012. [pdf] [poster] [slides] [talk] [appendix]

• Hybrid Deterministic-Stochastic Methods for Data Fitting.
  M. Friedlander, M. Schmidt. SISC, 2012. [pdf] [slides] [code] [addendum]

• A simpler approach to obtaining an O(1/t) convergence rate for projected stochastic subgradient descent.
  S. Lacoste-Julien, M. Schmidt, F. Bach. arXiv, 2012. [pdf]

• On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models.
  D. Buchmann, M. Schmidt, S. Mohamed, D. Poole, N. de Freitas. AISTATS, 2012. [pdf] [poster] [code].

2011

• Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization.
  M. Schmidt, N. Le Roux, F. Bach. NIPS, 2011. [pdf] [poster] [slides] [talk] [code]

• 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. [pdf] [slides] [talk] [code]

• Generalized Fast Approximate Energy Minimization via Graph Cuts: Alpha-Expansion Beta-Shrink Moves.
  M. Schmidt, K. Alahari. UAI, 2011. [pdf] [poster] [code]

2010

• Graphical Model Structure Learning with L1-Regularization.
  M. Schmidt. PhD Thesis, 2010. [pdf] [slides] [code]

• Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials.
  M. Schmidt, K. Murphy. AISTATS, 2010. [pdf] [slides] [talk] [code]

• Modeling annotator expertise: Learning when everybody knows a bit of something.
  Y. Yan, R. Rosales, G. Fung, M. Schmidt, G. Hermosillo, L. Bogoni, L. Moy, J. Dy. AISTATS, 2010. [pdf] [talk]

• Causal Learning without DAGs.
  D. Duvenaud, D. Eaton, K. Murphy, M. Schmidt. JMLR W&CP, 2010. [pdf] [poster] [code]

2009

• Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm.
  M. Schmidt, E. van den Berg, M. Friedlander, K. Murphy. AISTATS, 2009 (Best Paper Award) [pdf] [slides] [code] [examples]

• Group Sparse Priors for Covariance Estimation.
  B. Marlin, M. Schmidt, K. Murphy. UAI, 2009. [pdf] [poster]

• Modeling Discrete Interventional Data using Directed Cyclic Graphical Models.
  M. Schmidt, K. Murphy. UAI, 2009. [pdf] [slides] [code] [addendum]

• Increased Discrimination in Level Set Methods with Embedded Conditional Random Fields.
  D. Cobzas, M. Schmidt. CVPR, 2009. [pdf] [poster]

• Optimization Methods for L1-Regularization.
  M. Schmidt, G. Fung, R. Rosales. UBC Technical Report, 2009. [pdf] [code] [examples]

2008

• Structure Learning in Random Fields for Heart Motion Abnormality Detection.
  M. Schmidt, K. Murphy, G. Fung, R. Rosales. CVPR, 2008. [pdf] [poster] [code] [addendum]

• Group Sparsity via Linear-Time Projection.
  E. van den Berg, M. Schmidt. M. Friedlander, K. Murphy. UBC Technical Report, 2008. [pdf] [code]

• An interior-point stochastic approximation method and an L1-regularized delta rule.
  P. Carbonetto, M. Schmidt, N. de Freitas. NIPS, 2008. [pdf] [slides] [code]

2007

• Fast Optimization Methods for L1-Regularization: A Comparative Study and 2 New Approaches.
  M. Schmidt, G. Fung, R. Rosales. ECML, 2007. [pdf] [slides] [talk] [code] [examples] [addendum] [extended version]

• Learning Graphical Model Structure using L1-Regularization Paths.
  M. Schmidt, A. Niculescu-Mizil, K Murphy. AAAI, 2007. [pdf] [code] [addendum]

• 3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set.
  D. Cobzas, N. Birkbeck, M. Schmidt, M. Jagersand, A. Murtha. MMBIA, 2007. [pdf] [online material]

2006

• Accelerated Training of Conditional Random Fields with Stochastic Gradient Methods.
  S. Vishwanathan, N. Schraudolph, M. Schmidt, K. Murphy. ICML, 2006. [pdf] [1d code] [2d code] [slides]

• A Classification-based Glioma Diffusion Model Using MRI Data.
  M. Morris, R. Greiner, J. Sander, A. Murtha, M. Schmidt. CAI, 2006. [pdf]

2005

• Segmenting Brain Tumors using Conditional Random Fields and Support Vector Machines.
  C.-H. Lee, M. Schmidt, A. Murtha, A. Bistritz, J. Sander, R. Greiner. CVBIA, 2005. [pdf] [poster]

• Segmenting Brain Tumors using Alignment-Based Features.
  M. Schmidt, I. Levner, R. Greiner, A. Murtha, A. Bistritz. ICMLA, 2005. [pdf]

• Support Vector Random Fields for Spatial Classification.
  C.-H. Lee, R. Greiner, M. Schmidt. PKDD, 2005. [pdf] [presentation]

• Automatic Brain Tumor Segmentation.
  M. Schmidt. MSc Thesis, 2005. [pdf]

Notes

• Faster Stochastic Gradient under Stronger Conditions (2012)

• Big-N Problems (2012)

• Batching and Shrinking with the Hinge Loss (2012)

• Convex Optimization (MLSS Practical Sessions) (2011)

• Convergence Rates (2010)

• Singular Value Thresholding (2010)

• Generalized Interventional Potentials (2010)

• Polar Cones (2009)

• Linear Algebra (2009)

• Structural SVMs (2009)

• Lasso Duals (2008)

• Projection onto L1,Q-Norm Cones (2008)

• LARS-MLE, L1PC (2007)