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A list of topics for a google summer of code (gsoc) 2012
Possible mentor: Olivier Grisel
Possible candidate: Vlad Niculae, ?
Goal: Online or Minibatch SGD or similar on a squared l2 reconstruction loss + low rank penalty (nuclear norm) on scipy.sparse matrix: the implicit components of the sparse input representation would be interpreted by the algorithms as missing values rather that zero values.
Application: Build a scalable recommender system example, e.g. on the movielens dataset.
TODO: find references in the literature.
Possible mentor: Olivier Grisel
Possible candidate: Vlad Niculae, ?
Goal: Online or Minibatch NMF using SGD + positive projections (or any other out-of-core algorithms) accepting both dense and sparse matrix as input (decomposition components can be dense array only).
Application: Build a scalable topic model e.g. on million of Wikipedia abstracts for instance using this script.
TODO: find references in the literature.
Algorithms for decomposing a design matrix into a low rank + sparse components.
Possible mentor: ?
Possible candidate: Kerui Min (Minibio: "I'm a graduate student at UIUC who is currently pursuing the research work related to low-rank matrices recovery & Robust PCA.")
Applications: ?
References:
- http://perception.csl.uiuc.edu/matrix-rank/home.html
- http://www.icml-2011.org/papers/41_icmlpaper.pdf (randomized algorithm supposedly scalable to larg-ish datasets)
Possible mentor: Andreas Mueller
Goal: Implement a stochastic gradient descent algorithm to learn a multi-layer perceptron.
References:
- http://en.wikipedia.org/wiki/Multi-layer_perceptron
- http://www.springerlink.com/content/4w0bab2v3qnqhwyr/
Possible mentor: Andreas Mueller
Goal: Implement a stochastic gradient descent SVM using a low-rank kernel approximation.
References:
Possible mentor: Paolo Losi (others?)
Goal: Implement one of the state of art methods for Generalized Additive Models
References: