lcsl Goto Github PK
Name: Laboratory for Computational and Statistical Learning
Type: Organization
Location: Massachusetts Institute of Technology, Bldg. 46-5155, 43 Vassar Street, Cambridge, MA
Blog: http://lcsl.mit.edu/
Name: Laboratory for Computational and Statistical Learning
Type: Organization
Location: Massachusetts Institute of Technology, Bldg. 46-5155, 43 Vassar Street, Cambridge, MA
Blog: http://lcsl.mit.edu/
Fast algorithm for leverage score sampling, low rank (kernel) matrix factorization and PCA
Experiments for the first very fast and exact DPP sampler (Dereziński M, Calandriello D, Valko M. Exact sampling of determinantal point processes with sublinear time preprocessing. NeurIPS 2019)
FALKON implementation used in the experimental section of "FALKON: An Optimal Large Scale Kernel Method"
GURLS: a Least Squares Library for Supervised Learning
Camoriano, R.^, Pasquale, G.^, Ciliberto, C., Natale, L., Rosasco, L. and Metta, G., Incremental robot learning of new objects with fixed update time. In IEEE International Conference on Robotics and Automation (ICRA), May 2017.
Iterative regularization solvers for non strongly convex penalties : L1, low rank, etc.
Multi Task Learning Package for Matlab
This repository contains the implementations of the MLCC laboratories in python
2017
Lin, J., Camoriano, R. and Rosasco, L., Generalization properties and implicit regularization for multiple passes SGM. In International Conference on Machine Learning, June 2016.
Nystroem-based Koopman operator regression for linear quadratic control.
Rudi, A., Camoriano, R. and Rosasco, L., Less is more: Nyström computational regularization. In Advances in Neural Information Processing Systems, December 2015.
Camoriano, R.^, Angles, T.^, Rudi, A. and Rosasco, L., Nytro: When subsampling meets early stopping. In Artificial Intelligence and Statistics, May 2016.
Random feature approximation of heteroscedastic Gaussian processes (CoRL 2023).
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