Database-friendly random projections: Johnson-Lindenstrauss with binary coins, Journal of Computer and System Sciences, vol.66, issue.4, pp.671-687, 2003. ,
DOI : 10.1016/S0022-0000(03)00025-4
Fast Dimension Reduction Using Rademacher Series on??Dual BCH Codes, Discrete & Computational Geometry, vol.511, issue.34, pp.615-630, 2009. ,
DOI : 10.1007/s00454-008-9110-x
Simple Constructions of Almost k-wise Independent Random Variables, Random Structures and Algorithms, vol.6, issue.3, pp.289-304, 1992. ,
DOI : 10.1002/rsa.3240030308
The space complexity of approximating the frequency moments, Pages 20?29 of: Proceedings of the twenty-eighth annual ACM symposium on Theory of computing, 1996. ,
Testing k-wise and almost k-wise independence, Proceedings of the thirty-ninth annual ACM symposium on Theory of computing , STOC '07, 2007. ,
DOI : 10.1145/1250790.1250863
Compressive sensing, 2008 42nd Annual Conference on Information Sciences and Systems, 2007. ,
DOI : 10.1109/CISS.2008.4558479
URL : https://hal.archives-ouvertes.fr/hal-00452261
Towards scaling up MCMC: an adaptive subsampling approach, Proceedings of the International Conference on Machine Learning (ICML), 2014. ,
On Markov chain Monte Carlo methods for tall data, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01355287
Concentration Inequalities, 2013. ,
DOI : 10.1007/978-1-4757-2440-0
URL : https://hal.archives-ouvertes.fr/hal-00751496
Handbook of Markov Chain Monte Carlo, 2011. ,
DOI : 10.1201/b10905
Near-optimal signal recovery from random projections: Universal encoding strategies? Information Theory, IEEE Transactions on, vol.52, issue.12, pp.5406-5425, 2006. ,
DOI : 10.1109/tit.2006.885507
URL : http://arxiv.org/abs/math/0410542
Numerical linear algebra in the streaming model, Proceedings of the 41st annual ACM symposium on Symposium on theory of computing, STOC '09, 2009. ,
DOI : 10.1145/1536414.1536445
An elementary proof of a theorem of Johnson and Lindenstrauss. Random structures and algorithms, pp.60-65, 2003. ,
Compressed sensing. Information Theory, IEEE Transactions on, vol.52, issue.4, pp.1289-1306, 2006. ,
URL : https://hal.archives-ouvertes.fr/inria-00369486
Multiscale random projections for compressive classification . Pages VI?161 of: Image Processing, IEEE International Conference on, 2007. ,
Random projections for Bayesian regression, Statistics and Computing, vol.21, issue.4, 2015. ,
DOI : 10.1080/01621459.2014.969425
One sketch for all, Proceedings of the thirty-ninth annual ACM symposium on Theory of computing , STOC '07, 2007. ,
DOI : 10.1145/1250790.1250824
Modelling sparse dynamical systems with compressed predictive state representations, Pages 178?186 of: Proceedings of the 30th International Conference on Machine Learning (ICML-13), 2013. ,
Sampling for Bayesian Computation with Large Datasets, SSRN Electronic Journal, 2005. ,
DOI : 10.2139/ssrn.1010107
Extensions of Lipschitz mappings into a Hilbert space, Contemporary Mathematics, vol.26, pp.189-206, 1984. ,
DOI : 10.1090/conm/026/737400
Austerity in MCMC Land: Cutting the Metropolis- Hastings Budget, Proceedings of the International Conference on Machine Learning (ICML), 2014. ,
Firefly Monte Carlo: Exact MCMC with Subsets of Data, Proceedings of the conference on Uncertainty in Artificial Intelligence (UAI), 2014. ,
Linear Regression with Random Projections, Journal of Machine Learning Research, vol.13, pp.2735-2772, 2012. ,
URL : https://hal.archives-ouvertes.fr/inria-00483014
Scalable and Robust Bayesian Inference via the Median Posterior, Proceedings of The International Conference on Machine Learning (ICML), 2014. ,
Data streams: algorithms and applications. Foundations and Trends in Theoretical Computer Science, 2005. ,
Asymptotically exact, embarassingly parallel MCMC, Proceedings of the conference on Uncertainty in Artificial INtelligence (UAI), 2014. ,
Speeding Up MCMC by Efficient Data Subsampling. Preprint, available as http, 2014. ,
Scalable MCMC for Large Data Problems using Data Subsampling and the Difference Estimator Speeding Up MCMC by Efficient Data Subsampling, 2015. ,
Monte Carlo Statistical Methods, 2004. ,
Optimal scaling for various Metropolis-Hastings algorithms, Statistical Science, vol.16, issue.4, pp.351-367, 2001. ,
DOI : 10.1214/ss/1015346320
Fast spectral clustering with random projection and sampling . Pages 372?384 of: Machine Learning and Data Mining in Pattern Recognition, 2009. ,
Improved Approximation Algorithms for Large Matrices via Random Projections, 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06), 2006. ,
DOI : 10.1109/FOCS.2006.37
Bayes and big data: the consensus Monte Carlo algorithm, Proceedings of the Bayes 250 conference, 2013. ,
DOI : 10.1109/CDC.2012.6426691
WASP: scalable Bayes via barycenters of subset posteriors, 2014. ,
Distributed sparse random projections for refinable approximation, Pages 331?339 of: Proceedings of the 6th international conference on Information processing in sensor networks, 2007. ,
Bayesian Learning via Stochastic Gradient Langevin Dynamics, Proceedings of the International Conference on Machine Learning (ICML), 2011. ,