We present a unified framework for reasoning about worst-case regret bounds for learning algorithms. This framework is based on the theory of duality of convex functions. It brin...
Abstract Most ranking algorithms are based on the optimization of some loss functions, such as the pairwise loss. However, these loss functions are often different from the criter...
While monitoring, instrumented long running parallel applications generate huge amount of instrumentation data. Processing and storing this data incurs overhead, and perturbs the ...
We present an extension of sparse PCA, or sparse dictionary learning, where the sparsity patterns of all dictionary elements are structured and constrained to belong to a prespeci...
Rodolphe Jenatton, Guillaume Obozinski, Francis Ba...
The exploration of multidimensional scalar fields is commonly based on the knowledge of the topology of their isosurfaces. The latter is established through the analysis of critic...