Key approaches in the rapidly developing area of sparse modeling, focusing on its application in fields including neuroscience, computational biology, and computer vision.
An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities.
An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions.
Solutions for learning from large scale datasets, including kernel learning algorithms that scale linearly with the volume of the data and experiments carried out on realistically large datasets.
State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.
Leading researchers in signal processing and neural computation present work aimed at promoting the interaction and cross-fertilization between the two fields.
Advances in computational geometry and machine learning that offer new methods for search, regression, and classification with large amounts of high-dimensional data.
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