Learning Kernel Classifiers

By Francis Bach, Ralf Herbrich

Gaussian Processes for Machine Learning

By Francis Bach, Carl Edward Rasmussen, Christopher K. I. Williams

Probabilistic Graphical Models

By Nir Friedman, Francis Bach, Daphne Koller

A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.Most tasks require a person or...

Boosting

By Francis Bach, Robert E. Schapire, Yoav Freund

An accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate ones.Boosting is an approa...

The Minimum Description Length Principle

By Peter D. Grunwald, Francis Bach

A comprehensive introduction and reference guide to the minimum description length (MDL) Principle that is accessible to researchers dealing with inductive reference in diverse areas including stat...

Learning with Kernels

By Alexander J. Smola, Francis Bach, Bernhard Scholkopf

A comprehensive introduction to Support Vector Machines and related kernel methods.In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: t...

Optimization with Sparsity Inducing Penalties

By Guillaume Obozinski, Francis Bach, Rodolphe Jenatton, Julien Mairal

Optimization with Sparsity Inducing Penalties

By Guillaume Obozinski, Francis Bach, Rodolphe Jenatton, Julien Mairal

Learning with Submodular Functions

By Francis Bach

Sparse Modeling for Image and Vision Processing

By Francis Bach, Jean Ponce, Julien Mairal