- Graph Kernels 1 and Graph Kernels 2 Code for our latest incarnations of graph kernels.
- NOMAD Asynchronous algorithm for large scale matrix factorization and LDA
- RobiRank Ranking with millions of items
- StreamSVM Fastest optimizer for training linear models such as SVM, Logistic Regression, and Support Vector Regression when data does not fit in memory
- SMS An algorithm for optimizing multivariate performance measure by smoothing the loss. Works in a parallel and distributed environment
- SPG-GMKL Spectral Projected Gradient Descent for the Generalized Multiple Kernel Learning Problem
- SMO-MKL A variant of the SMO algorithm for the Multiple Kernel Learning Problem. Can handle millions of kernels.
- ERLPBoost A totally corrective boosting algofffgbvv rithm that scales to large datasets.
- subBFGS A variant of the BFGS algorithm for handling non-smooth regularized risk minimization problems the occur in machine learning.
- BMRM Bundle Methods for Machine Learning. One of the first multi-machine map-reduce architectures for machine learning. We implemented and released BMRM long before map-reduce became a buzzword!

In order to obtain code for my older papers not listed here please send me email. Note that for training Conditional Random Fields (CRFs) I recommend the excellent SGD implementation of Leon Bottou which can be found here.