Publications by Mark Schmidt

Refereed

Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials
M. Schmidt, K. Murphy
AISTATS'10

Modeling Discrete Interventional Data using Directed Cyclic Graphical Models
M. Schmidt, K. Murphy
UAI'09
Slides

Group Sparse Priors for Covariance Estimation
B. Marlin, M. Schmidt, K. Murphy
UAI'09
Poster

Increased Discrimination in Level Set Methods with Embedded Conditional Random Fields
D. Cobzas, M. Schmidt
CVPR'09
Poster

Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm
M. Schmidt, E. van den Berg, M. Friedlander, K. Murphy
AISTATS'09 (Best Paper Award)
Slides Software Examples

Structure Learning in Random Fields for Heart Motion Abnormality Detection
M. Schmidt, K. Murphy, G. Fung, R. Rosales
CVPR'08
Poster Software Addendum

An interior-point stochastic approximation method and an L1-regularized delta rule
P. Carbonetto, M. Schmidt, N. de Freitas
NIPS'08
Slides Software

Fast Optimization Methods for L1-Regularization: A Comparative Study and 2 New Approaches
M. Schmidt, G. Fung, R. Rosales
ECML'07
Slides Talk Software Examples Addendum Extended Version

Learning Graphical Model Structure using L1-Regularization Paths
M. Schmidt, A. Niculescu-Mizil, K Murphy.
AAAI'07
Software Addendum.

Accelerated Training of Conditional Random Fields with Stochastic Gradient Methods
S. Vishwanathan, N. Schraudolph, M. Schmidt, K. Murphy
ICML'06
Software Slides.

Support Vector Random Fields for Spatial Classification
C.-H. Lee, R. Greiner, M. Schmidt
PKDD'05
Presentation

Unrefereed or Lightly Refereed

Causal Learning without DAGs
D. Duvenaud, D. Eaton, K. Murphy, M. Schmidt
JMLR W&CP'10
Poster Software

Optimization Methods for L1-Regularization
M. Schmidt, G. Fung, R. Rosales
UBC TR-2009-19
Software Examples

A Note on Structural Extensions of SVMs
M. Schmidt

Group Sparsity via Linear-Time Projection
E. van den Berg, M. Schmidt. M. Friedlander, K. Murphy
UBC TR-2008-09
Software

Two Dual Problems to L1-Regularized Least Squares
M. Schmidt

3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set
D. Cobzas, N. Birkbeck, M. Schmidt, M. Jagersand, A. Murtha.
MMBIA'07
Online Material with images and demo.

LassoOrderSearch: Learning Directed Graphical Model Structure using L1-Penalized Regression and Order Search
M. Schmidt, K. Murphy
NIPS'06 Workshop on Causality and Feature Selection

A Classification-based Glioma Diffusion Model Using MRI Data
M. Morris, R. Greiner, J. Sander, A. Murtha, M. Schmidt
CAI'06

Segmenting Brain Tumors using Conditional Random Fields and Support Vector Machines
C.-H. Lee, M. Schmidt, A. Murtha, A. Bistritz, J. Sander, R. Greiner.
ICCV'05 Workshop on Computer Vision for Biomedical Image Applications
Poster

Segmenting Brain Tumors using Alignment-Based Features
M. Schmidt, I. Levner, R. Greiner, A. Murtha, A. Bistritz
ICMLA'05

Thesis

Graphical Model Structure Learning with L1-Regularization
M. Schmidt
PhD Thesis '10 (in progress)

Automatic Brain Tumor Segmentation
M. Schmidt
MSc Thesis '05



Mark Schmidt > Publications