|
Christophe Ambroise (CNRS) |
Inferring sparse gaussian graphical models for biological networks |
|
Francois Caron (Bordeaux) |
Hierarchical models for dependent sparse linear regressions |
|
Sara van de Geer (Zurich) |
The Lasso with within group structure |
|
Chris Holmes (Oxford) |
Bayesian nonparametric clustering of sparse signals |
|
Ann Lee (Carnegie Mellon) |
Exploiting sparse structure by spectral connectivity analysis |
|
David Madigan (Columbia) |
Bayesian methods for drug safety surveillance |
|
Alexandre Tsybakov (Paris VI) |
Estimation of high-dimensional low rank matrices |
|
Martin Wainwright (Berkeley) |
Graphical model selection in high dimensions: Practical and information-theoretic limits |
|
Marten Wegkamp (Florida State) |
Adaptive rank penalized estimators in multivariate regression |
|
Nicolas Brunel (Evry) |
Sparse autoregressive models for module extraction in biological networks |
|
Colin Campbell (Bristol) |
Multiple kernel learning methods for handling large and complex datasets |
|
Haeran Cho (LSE) |
High-dimensional variable selection via tilting |
|
Barbara Engelhardt (Chicago) |
Sparse factor analysis applied to biological problems |
|
Florian Frommlet (Vienna) |
Asymptotic Bayes optimality under sparsity of multiple testing and model selection procedures |
|
Paul Kirk (Imperial) |
Stability selection methods for biomarker discovery |
|
Keith Knight (Toronto) |
Adaptive lasso for correlated predictors |
|
Silvia Liverani(Bristol) |
Bayesian model selection on high-dimensional time series |
|
Guillaume Obozinski (INRIA) |
Structured sparse principal component analysis |
|
Jianxin Pan (Manchester) |
Modelling of large covariance matrices |
|
Kevin Sharp (Manchester) |
Dense message passing for sparse principal component analysis |
|
Andrew Smith (Bristol) |
Penalised regression on a graph |
|
Sarel Steel (Stellenbosch) |
Variable selection for kernel classification |
|
Vanna Albieri (Danish Cancer Society) |
A comparison of structural learning procedures for biological networks |
|
Luke Bornn (British Columbia) |
The product graphical model |
|
Colin Campbell (Bristol) |
Sparse regularisation methods for metric learning |
|
Sohail Chand (Nottingham) |
Oracle properties of lasso-type methods |
|
Tom Diethe (University College London) |
Learning in a Nystrom approximated space |
|
Zhou Fang (Oxford) |
Group sparsity through concave penalties |
|
Marie Fitch (Massey University, NZ) |
Sparsity vs computational convenience for estimation of a sparse inverse covariance matrix |
|
Doyo Gragn (Open University) |
Sparse principal components based on semi-divisive clustering of genes |
|
Shota Gugushvili (Vrije Universiteit Amsterdam) |
√n-consistent estimation for systems of ordinary differential equations: bypassing numerical
integration via smoothing |
|
Edmund Jones (Bristol) |
Graph distributions for Bayesian learning of sparse graphical model structures |
|
Shakir Mohamed (Cambridge) |
Bayesian learning with correlated spike-and-slab priors |
|
Dino Sejdinovic (Bristol) |
Message-passing algorithms in coding and information theory |
