Laboratory for Computational and Statistical Learning (LCSL)

Laboratory for Computational and Statistical Learning

LCSL

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Location: Massachusetts Institute of Technology, Bldg. 46-5155, 43 Vassar Street, Cambridge, MA

Home Page:http://lcsl.mit.edu/

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Laboratory for Computational and Statistical Learning's repositories

GURLS

GURLS: a Least Squares Library for Supervised Learning

FALKON_paper

FALKON implementation used in the experimental section of "FALKON: An Optimal Large Scale Kernel Method"

bless

Fast algorithm for leverage score sampling, low rank (kernel) matrix factorization and PCA

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NystromCoRe

Rudi, A., Camoriano, R. and Rosasco, L., Less is more: Nyström computational regularization. In Advances in Neural Information Processing Systems, December 2015.

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dpp-vfx

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)

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incremental_multiclass_RLSC

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.

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iterreg

Iterative regularization solvers for non strongly convex penalties : L1, low rank, etc.

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NYTRO

Camoriano, R.^, Angles, T.^, Rudi, A. and Rosasco, L., Nytro: When subsampling meets early stopping. In Artificial Intelligence and Statistics, May 2016.

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matMTL

Multi Task Learning Package for Matlab

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MultiplePassesSGM

Lin, J., Camoriano, R. and Rosasco, L., Generalization properties and implicit regularization for multiple passes SGM. In International Conference on Machine Learning, June 2016.

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rff-hgp

Random feature approximation of heteroscedastic Gaussian processes (CoRL 2023).

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nys-koop-lqr

Nystroem-based Koopman operator regression for linear quadratic control.

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MLCC-Labs

This repository contains the implementations of the MLCC laboratories in python

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