R. H. Affandi, A. Kulesza, E. B. Fox, and B. Taskar, Nyström approximation for large-scale determinantal processes, International Conference on Artificial Intelligence and Statistics, pp.85-98, 2013.

D. J. Aldous, The Random Walk Construction of Uniform Spanning Trees and Uniform Labelled Trees, SIAM Journal on Discrete Mathematics, vol.3, issue.4, pp.450-465, 1990.
DOI : 10.1137/0403039

N. Anari, S. O. Gharan, and A. Rezaei, Monte-Carlo Markov chain algorithms for sampling strongly Rayleigh distributions and determinantal point processes, Conference on Learning Theory, pp.23-26, 2016.

H. C. Andersen and P. W. Diaconis, Hit and run as a unifying device, Journal de la Société Française de Statistique, pp.5-28, 2007.

M. Andersen, J. Dahl, and L. Vandenberghe, CVXOPT: A python package for convex optimization, 2008.

A. Barabási and R. Albert, Emergence of scaling in random networks, Science, vol.286, issue.11, 1999.

R. Bardenet and A. Hardy, Monte-Carlo with determinantal point processes. arXiv preprint, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01311263

A. Broder, Generating random spanning trees, 30th Annual Symposium on Foundations of Computer Science, pp.442-447, 1989.
DOI : 10.1109/SFCS.1989.63516

R. Burton and R. Pemantle, Local characteristics, entropy and limit theorems for spanning trees and domino tilings via transfer impedances. The Annals of Probability, pp.1329-1371, 1993.
DOI : 10.1214/aop/1176989121
URL : http://arxiv.org/abs/math/0404048

B. Cousins and S. Vempala, A practical volume algorithm, Mathematical Programming Computation, vol.27, issue.2, pp.133-160, 2016.
DOI : 10.1007/s12532-015-0097-z

A. Deshpande and L. Rademacher, Efficient Volume Sampling for Row/Column Subset Selection, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science, 2010.
DOI : 10.1109/FOCS.2010.38
URL : http://arxiv.org/abs/1004.4057

C. Dupuy and F. Bach, Learning determinantal point processes in sublinear time. arXiv preprint, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01383742

M. Dyer and A. Frieze, Random walks, totally unimodular matrices, and a randomised dual simplex algorithm, Mathematical Programming, pp.1-16, 1994.
DOI : 10.1007/BF01582563
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.7936

T. Feder and M. Mihail, Balanced matroids, Proceedings of the twenty-fourth annual ACM symposium on Theory of computing , STOC '92, pp.26-38, 1992.
DOI : 10.1145/129712.129716

M. Gartrell, U. Paquet, and N. Koenigstein, Low-rank factorization of determinantal point processes for recommendation, AAAI Conference on Artificial Intelligence, p.1912, 1918.

A. Gelman and D. B. Rubin, Inference from Iterative Simulation Using Multiple Sequences, Statistical Science, vol.7, issue.4, pp.457-472, 1992.
DOI : 10.1214/ss/1177011136

J. Gillenwater, A. Kulesza, and B. Taskar, Discovering diverse and salient threads in document collections, Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp.710-720, 2012.

J. B. Hough, M. Krishnapur, Y. Peres, and B. Virág, Determinantal processes and independence. Probability surveys, 2006.
DOI : 10.1214/154957806000000078
URL : http://arxiv.org/abs/math/0503110

B. Kang, Fast determinantal point process sampling with application to clustering, Neural Information Processing Systems, pp.2319-2327, 2013.

T. Kathuria, A. Deshpande, and P. Kohli, Batched gaussian process bandit optimization via determinantal point processes, Neural Information Processing Systems, pp.4206-4214, 2016.

A. Kulesza and B. Taskar, Determinantal Point Processes for Machine Learning, Machine Learning, pp.123-286, 2012.
DOI : 10.1561/2200000044
URL : http://arxiv.org/abs/1207.6083

A. Kulesza and B. Taskar, k-dpps: Fixed-size determinantal point processes, International Conference on Machine Learning, pp.1193-1200, 2011.

F. Lavancier, J. Møller, R. , and E. , Determinantal point process models and statistical inference, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.4, issue.4, pp.853-877, 2015.
DOI : 10.1111/rssb.12096
URL : https://hal.archives-ouvertes.fr/hal-01241077

D. A. Levin, Y. Peres, W. , and E. L. , Markov chains and mixing times, 2009.
DOI : 10.1090/mbk/058

C. Li, S. Jegelka, and S. Sra, Efficient sampling for kdeterminantal point processes, Artificial Intelligence and Statistics, pp.1328-1337, 2016.

C. Li, S. Jegelka, and S. Sra, Fast sampling for strongly rayleigh measures with application to determinantal point processes, Neural Information Processing Systems, pp.4188-4196, 2016.

D. Liang and J. Paisley, Landmarking manifolds with gaussian processes, International Conference on Machine Learning, pp.466-474, 2015.

E. Loper, S. Bird, and . Nltk, The natural language toolkit, Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, pp.63-70, 2002.

D. G. Luenberger and Y. Ye, Linear and nonlinear programming, 2008.
DOI : 10.1007/978-3-319-18842-3

R. Lyons, Determinantal probability measures, Publications math?matiques de l'IH?S, vol.98, issue.1, 2003.
DOI : 10.1007/s10240-003-0016-0
URL : http://arxiv.org/abs/math/0204325

O. Macchi, The coincidence approach to stochastic point processes, Advances in Applied Probability, vol.271, issue.01, pp.83-122, 1975.
DOI : 10.1103/RevModPhys.37.231

R. M. Neal, Slice sampling. Annals of statistics, pp.705-741, 2003.
DOI : 10.1214/aos/1056562461

E. Oki, Gnu linear programming kit, version 4.61, Linear Programming and Algorithms for Communication Networks -A Practical Guide to Network Design, Control, and Management, 2012.

J. Oxley, What is a matroid? Cubo Matemática Educacional, pp.179-218, 2003.

M. Plummer, N. Best, K. Cowles, and K. Vines, Coda: Convergence diagnosis and output analysis for MCMC, R News, vol.6, issue.1, pp.7-11, 2006.

J. G. Propp and D. B. Wilson, How to Get a Perfectly Random Sample from a Generic Markov Chain and Generate a Random Spanning Tree of a Directed Graph, Journal of Algorithms, vol.27, issue.2, pp.170-217, 1998.
DOI : 10.1006/jagm.1997.0917

P. Rebeschini and A. Karbasi, Fast mixing for discrete point processes, Conference on Learning Theory, pp.1480-1500, 2015.

C. P. Robert and G. Casella, Monte-Carlo Statistical Methods, 2004.

Z. Rudnick and P. Sarnak, Zeros of principal $L$ -functions and random matrix theory, Duke Mathematical Journal, vol.81, issue.2, pp.269-322, 1996.
DOI : 10.1215/S0012-7094-96-08115-6

R. L. Smith, Efficient Monte Carlo Procedures for Generating Points Uniformly Distributed over Bounded Regions, Operations Research, vol.32, issue.6, pp.1296-1308, 1984.
DOI : 10.1287/opre.32.6.1296

J. Snoek, R. Zemel, A. , and R. P. , A determinantal point process latent variable model for inhibition in neural spiking data, Neural Information Processing Systems, pp.1932-1940, 2013.

V. F. Tur?in, On the computation of multidimensional integrals by the monte-carlo method. Theory of Probability & Its Applications, pp.720-724, 1971.

C. Williams and M. Seeger, Using the Nyström method to speed up kernel machines, Neural Information Processing Systems, pp.682-688, 2001.

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