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Seminar SIT55 -- November 29th, 2011, 14h00, Salle Master

Sparsity in learning

by Yves GRANDVALET, DR CNRS researcher


Machine learning aims at discovering regularities from examples. In this process, sparsity can be introduced from different perspectives. For a given task, it can target (1) computational efficiency, by avoiding to process insignificant pieces of information; (2) interpretability, by putting forward the salient pieces of information; (3) prediction accuracy, by introducing an induction bias preventing overfitting to the training examples. I will consider two facets of sparsity, by looking at the two dimensions of the data table that represents the training sample: examples and variables. We will see how these two approaches can be formalized and motivated from the theoretical point of view. I will then focus on some properties and practical issues and finally conclude by some open questions on the topic.


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