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Tutorial - Structured sparsity-inducing norms through submodular functions

Thursday, September 29, 2011 - 9:00am - 9:45am
Keller 3-180
Francis Bach (École Normale Supérieure)
Sparse methods for supervised learning aim at finding good
linear predictors from as few variables as possible, i.e., with small
cardinality of their supports. This combinatorial selection problem is
often turned into a convex optimization problem by replacing the
cardinality function by its convex envelope (tightest convex lower bound),
in this case the L1-norm. In this work, we investigate more general set-functions than the cardinality, that may incorporate prior knowledge
or structural constraints which are common in many applications:
namely, we show that for nondecreasing submodular set-functions, the
corresponding convex envelope can be obtained from its Lovasz extension,
a common tool in submodular analysis. This defines a family of polyhedral
norms, for which we provide generic algorithmic tools (subgradients
and proximal operators) and theoretical results (conditions for support
recovery or high-dimensional inference). By selecting specific submodular
functions, we can give a new interpretation to known norms, such as those
based on rank-statistics or grouped norms with potentially overlapping
groups; we also define new norms, in particular ones that can be used
as non-factorial priors for supervised learning.
MSC Code: 
65F50
Keywords: