Software by Mark Schmidt and Students
- code.zip - Compilation of updated and interoperable versions of many of the Matlab codes on this webpage.
- SLS - Python code implementing stochastic gradient with a stochastic line-search to set the step size.
- VB-MixEF - Matlab code for variational Bayes with a mixture of exponential family approximating distribution.
- FPEI - Python code for parameter estimation in nucleic acid reactions in the Multistrand simulator.
- BCDLaplacian - Python code for block coordinate descent updates using Laplacian-structured Hessian approximations
- GP_DRF - Python code for deep Gaussian proceess models with variable-sized inputs.
- OD-test - Python code for outlier detection.
- LCFCN - Python code for object counting in images from point annotations.
- MASAGA - Python code for stochastic optimization of finite sums on manifolds.
- BCD - Python code implementing various forms of block coordinate descent.
- IMBandits - Python code for influence maximization with bandit algorithms.
- ArrheniusInference - Python code for estimating kinematics of DNA molecules.
- matLearn - Matlab implementations of a wide variety of the most commonly-used machine learning algorithms.
- style-swap - LuaTorch code implementing a fast artistic style transfer method that allows new styles.
- Kaczmarz - Python code for randomized and greedy Kaczmarz methods for solving linear systems.
- SAG4CRF - Matlab mex files implementing a non-uniform stochastic average gradient method for L2-regularized chain-structured conditional random fields.
- practicalSVRG - Matlab mex files for stochastic variance-reduced gradient with growing batch size and identifying support vectors.
- GCD - Python code implementing Gauss-Southwell and Gauss-Southwell-Lipshcitz coordinate descent methods for linear models.
- SSL - Python files implementing semi-supervised learning (Yarowksy and co-training) approaches for some text-labeling problems (word-sense disambiguation and named-entity recognition)
- SAG - Matlab mex files implementing the stochastic average gradient method for L2-regularized logistic regression.
- Updated: prettyPlot - Added the ability to directly place labels on lines, and show filled-in error area.
- prettyPlot - A wrapper that uses Matlab's plot function to make plots that look nicer in papers.
- Updated: minFunc - Faster core L-BFGS code, enhanced derivative-checking options, and some minor changes.
- Updated: thesis - Added the canonical and spectral binary parameterizations to the log-linear model (LLM) code.
- Examples: QNST - A set of examples showing how to use QNST to solve a variety of problems.
- Examples: TMP - A set of examples showing how to use TMP to solve a variety of problems.
- An optimizer that combines an L-BFGS line-search method with a growing batch-size strategy.
- alphaBeta - Functions for approximate pairwise energy minimization with attractive
- thesis -
Quasi-Newton methods for non-differentiable optimization,
and structure learning in graphical models.
- inexactProx - Code to reproduce the experiments in the inexact proximal-gradient paper.
- Updated: UGM - Major update, including a complete change to the way paramter estimation is performed.
- Updated: L1General - New methods added, mostly focusing on limited-memory quasi-Newton and first-order methods.
- Updated: PQN - Fixed an important bug regarding the initialization of the SPG iterations.
- Functions implementing exact and approximate decoding, inference,
sampling, and parameter estimation in discrete undirected graphical models.
- minConf - Functions for optimization of differentiable real-valued
multivariate functions with simple constraints.
- DCG - A probabilistic model of interventional data that allows cycles.
- PQN - Code for optimization of (costly) differentiable multivariate functions over simple convex sets.
- Updated: minFunc - another set of minor changes in response to feedback, but also some non-trivial changes to the conjugate gradient and dense quasi-Newton methods.
- Examples: PQN - A set of examples showing how to use PQN to solve a variety of problems.
- Examples: L1General - A set of examples showing how to use L1General to solve a variety of problems.
- crfChain - Learning/inference/decoding/sampling in
chain-structured conditional random fields with categorical features.
- pdfCropAll - Runs pdfcrop on every .pdf in the current directory and replaces the original files.
- Matlab code for structure learning in discrete-state undirected graphical models (Markov
Random Fields and Conditional Random Fields) using Group L1-regularization.
- Gsparse - Matlab
functions implementing spectral projected gradient methods for optimization
with a Group L1-norm constraint.
- Updated: L1General - Added new methods and variants of all methods that don't require the exact Hessian.
- Updated: lasso - Added a variant of the active set method that allows warm-starting.
- Updated: minFunc - Most of the changes were minor and in response to user feedback.
- Examples: minFunc - A set of examples showing how to use minFunc to solve a variety of problems.
- L1General -
Functions implementing strategies for minimizing differentiable functions
subject to L1 regularization.
- Matlab function for L1-regularized estimation of the precision matrix in
Gaussian graphical models.
Matlab functions for structure learning in
Gaussian/Sigmoid directed acyclic graphical models.
- CRF2D -
Matlab code for inference and learning in 2D binary conditional
random fields, using approximate likelihoods (made obsolete by UGM).
- Functions for MCMC simulation of binary probit/logistic regression
distributions over parameters.
- lasso - Functions implementing a
variety of the methods available to solve 'LASSO' regression (and basis
- minFunc - Function for
optimization of differentiable real-valued multivariate functions.
Mark Schmidt > Software