Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks

no code implementations • NeurIPS 2014 • Mario Marchand, Hongyu Su, Emilie Morvant, Juho Rousu, John S. Shawe-Taylor

We show that the usual score function for conditional Markov networks can be written as the expectation over the scores of their spanning trees.

PAC-Bayesian Analysis of Contextual Bandits

no code implementations • NeurIPS 2011 • Yevgeny Seldin, Peter Auer, John S. Shawe-Taylor, Ronald Ortner, François Laviolette

The scaling of our regret bound with the number of states (contexts) $N$ goes as $\sqrt{N I_{\rho_t}(S;A)}$, where $I_{\rho_t}(S;A)$ is the mutual information between states and actions (the side information) used by the algorithm at round $t$.

Multi-Armed Bandits

Theory of matching pursuit

no code implementations • NeurIPS 2008 • Zakria Hussain, John S. Shawe-Taylor

We analyse matching pursuit for kernel principal components analysis by proving that the sparse subspace it produces is a sample compression scheme.

Variational Inference for Diffusion Processes

no code implementations • NeurIPS 2007 • Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John S. Shawe-Taylor

Diffusion processes are a family of continuous-time continuous-state stochastic processes that are in general only partially observed.

Variational Inference