Fetching the paper…
Reading the bibliography…
We study the problem of learning a distribution from samples, when the underlying distribution is a mixture of product distributions over discrete domains.
A. P. Dawid and A. M. Skene, Maximum likelihood estimation of observer error-rates using the em algorithm , Journal of the Royal Statistical Society. Series C (Applied Statistics) 28
1979
Earlier work this paper cites.
M. Kearns, Y. Mansour, D. Ron, R. Rubinfeld, R. E. Schapire, and L. Sellie, On the learnability of discrete distributions , STOC, 1994, pp. 273–282
1994
Earlier work this paper cites.
P. Smyth, U. Fayyad, M. Burl, P. Perona, and P. Baldi, Inferring ground truth from subjective labelling of venus images , NIPS, 1995, pp. 1085–1092
1995
Earlier work this paper cites.
Siu L Hui and Xiao H Zhou, Evaluation of diagnostic tests without gold standards , Statistical methods in medical research 7
1998
Earlier work this paper cites.
Y. Freund and Y. Mansour, Estimating a mixture of two product distributions , COLT, 1999, pp. 53–62
1999
Earlier work this paper cites.
S. Arora and R. Kannan, Learning mixtures of arbitrary Gaussians , STOC, 2001, pp. 247–257
2001
Earlier work this paper cites.
Frank McSherry, Spectral partitioning of random graphs , FOCS, 2001, pp. 529–537
2001
Earlier work this paper cites.
2004
Earlier work this paper cites.
Dimitris Achlioptas and Frank McSherry, On spectral learning of mixtures of distributions , Learning Theory, Springer, 2005, pp. 458–469
2005
Earlier work this paper cites.
Kamalika Chaudhuri, Eran Halperin, Satish Rao, and Shuheng Zhou, A rigorous analysis of population stratification with limited data , SODA, 2007, pp. 1046–1055
2007
Earlier work this paper cites.
S. Sridhar, S. Rao, and E. Halperin, An efficient and accurate graph-based approach to detect population substructure , Research in Computational Molecular Biology, 2007, pp. 503–517
2007
Cited alongside, same era.
K. Chaudhuri and S. Rao, Learning mixtures of product distributions using correlations and independence. , COLT, 2008, pp. 9–20
2008
Cited alongside, same era.
J. Feldman, R. O’Donnell, and R. A Servedio, Learning mixtures of product distributions over discrete domains , SIAM Journal on Computing 37
2008
Cited alongside, same era.
V. S. Sheng, F. Provost, and P. G. Ipeirotis, Get another label? improving data quality and data mining using multiple, noisy labelers , KDD, 2008, pp. 614–622
2008
Cited alongside, same era.
Ankur Moitra and Gregory Valiant, Settling the polynomial learnability of mixtures of Gaussians , FOCS, 2010, pp. 93–102
2010
Sanjeev Arora, Rong Ge, Ankur Moitra, and Sushant Sachdeva, Provable ICA with unknown Gaussian noise, with implications for Gaussian mixtures and autoencoders , NIPS, 2012, pp. 2384–2392
2012
Later among the works it cites.
2012
Later among the works it cites.
Navin Goyal and Luis Rademacher, Efficient learning of simplices , CoRR abs/1211.2227
2012
Later among the works it cites.
D. Hsu, S. M. Kakade, and T. Zhang, A spectral algorithm for learning hidden markov models , Journal of Computer and System Sciences 78
2012
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Dan-Cristian Tomozei and Laurent Massoulié, Distributed user profiling via spectral methods , ACM SIGMETRICS Performance Evaluation Review, vol. 38, 2010, pp. 383–384
2010
Cited alongside, same era.
A. Ghosh, S. Kale, and P. McAfee, Who moderates the moderators?: crowdsourcing abuse detection in user-generated content , EC, 2011, pp. 167–176
2011
Cited alongside, same era.
D. R. Karger, S. Oh, and D. Shah, Budget-optimal crowdsourcing using low-rank matrix approximations , Allerton, 2011
2011
Cited alongside, same era.
2012
Cited alongside, same era.
Sanjeev Arora, Rong Ge, and Ankur Moitra, Learning topic models - going beyond SVD , FOCS, 2012, pp. 1–10
2012
Cited alongside, same era.
2012
Later among the works it cites.
James Saunderson, Venkat Chandrasekaran, Pablo A. Parrilo, and Alan S. Willsky, Diagonal and low-rank matrix decompositions, correlation matrices, and ellipsoid fitting , SIAM J. Matrix Analysis Applications 33
2012
Later among the works it cites.
Joel A Tropp, User-friendly tail bounds for sums of random matrices , Foundations of Computational Mathematics 12
2012
Later among the works it cites.
Suriya Gunasekar, Ayan Acharya, Neeraj Gaur, and Joydeep Ghosh, Noisy matrix completion using alternating minimization , Machine Learning and Knowledge Discovery in Databases, Springer, 2013, pp. 194–209
2013
Closest in time.
D. Hsu and S. M. Kakade, Learning mixtures of spherical Gaussians: moment methods and spectral decompositions , ITCS, 2013, pp. 11–20
2013
Closest in time.
Prateek Jain, Praneeth Netrapalli, and Sujay Sanghavi, Low-rank matrix completion using alternating minimization , STOC, 2013, pp. 665–674
2013
Closest in time.