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The vast majority of theoretical results in machine learning and statistics assume that the available training data is a reasonably reliable reflection of the phenomena to be learned or estimated.
A survey of sampling from contaminated distributions
J. W. Tukey · 1960
Earlier work this paper cites.
Learning in the presence of malicious errors
M. Kearns and M. Li · 1993
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Coloring random and semi-random k-colorable graphs
A. Blum and J. Spencer · 1995
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Random vectors in the isotropic position
M. Rudelson · 1999
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Finding and certifying a large hidden clique in a semirandom graph
U. Feige and R. Krauthgamer · 2000
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Heuristics for semirandom graph problems
U. Feige and J. Kilian · 2001
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Spectral partitioning of random graphs
F. McSherry · 2001
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Spectral relaxation for k-means clustering
H. Zha, X. He, C. Ding, H. Simon, and M. Gu · 2001
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A tight bound on approximating arbitrary metrics by tree metrics
J. Fakcharoenphol, S. Rao, and K. Talwar · 2003
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Coloring semirandom graphs optimally
A. Coja-Oghlan · 2004
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On spectral learning of mixtures of distributions
D. Achlioptas and F. McSherry · 2005
Earlier work this paper cites.
Semi-Supervised Learning
O. Chapelle, A. Zien, and B. Scholkopf · 2006
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Semirandom models as benchmarks for coloring algorithms
M. Krivelevich and D. Vilenchik · 2006
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Solving NP-hard semirandom graph problems in polynomial expected time
A. Coja-Oghlan · 2007
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A discriminative framework for clustering via similarity functions
M. Balcan, A. Blum, and S. Vempala · 2008
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Agnostic clustering
M. F. Balcan, H. Röglin, and S. Teng · 2009
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On agnostic learning of parities, monomials, and halfspaces
V. Feldman, P. Gopalan, S. Khot, and A. K. Ponnuswami · 2009
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Hardness of learning halfspaces with noise
V. Guruswami and P. Raghavendra · 2009
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Robust Statistics
P. J. Huber and E. M. Ronchetti · 2009
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Learning halfspaces with malicious noise
A. R. Klivans, P. M. Long, and R. A. Servedio · 2009
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Clustering with spectral norm and the k-means algorithm
A. Kumar and R. Kannan · 2010
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Introduction to the non-asymptotic analysis of random matrices
The power of localization for efficiently learning linear separators with noise
P. Awasthi, M. F. Balcan, and P. M. Long · 2014
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Community detection in sparse networks via Grothendieck’s inequality
O. Guédon and R. Vershynin · 2014
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Multisection in the stochastic block model using semidefinite programming
N. Agarwal, A. S. Bandeira, K. Koiliaris, and A. Kolla · 2015
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Robust regression via hard thresholding
K. Bhatia, P. Jain, and P. Kar · 2015
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Robust and computationally feasible community detection in the presence of arbitrary outlier nodes
T. T. Cai and X. Li · 2015
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Concentration and regularization of random graphs
C. M. Le, E. Levina, and R. Vershynin · 2015
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R. Vershynin · 2010
Cited alongside, same era.
Principal component analysis with contaminated data: The high dimensional case
H. Xu, C. Caramanis, and S. Mannor · 2010
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Robust principal component analysis?
E. J. Candès, X. Li, Y. Ma, and J. Wright · 2011
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Rank-sparsity incoherence for matrix decomposition
V. Chandrasekaran, S. Sanghavi, P. A. Parrilo, and A. S. Willsky · 2011
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Robust Statistics: The Approach Based on Influence Functions
F. R. Hampel, E. M. Ronchetti, P. J. Rousseeuw, and W. A. Stahel · 2011
Cited alongside, same era.
Improved spectral-norm bounds for clustering
P. Awasthi and O. Sheffet · 2012
Cited alongside, same era.
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Learning communities in the presence of errors
K. Makarychev, Y. Makarychev, and A. Vijayaraghavan · 2015
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How robust are reconstruction thresholds for community detection?
A. Moitra, W. Perry, and A. S. Wein · 2015
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Coverings of random ellipsoids, and invertibility of matrices with iid heavy-tailed entries
E. Rebrova and K. Tikhomirov · 2015
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Robust estimators in high dimensions without the computational intractability
I. Diakonikolas, G. Kamath, D. Kane, J. Li, A. Moitra, and A. Stewart · 2016
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Finding meaningful cluster structure amidst background noise
S. Kushagra, S. Samadi, and S. Ben-David · 2016
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Agnostic estimation of mean and covariance
K. A. Lai, A. B. Rao, and S. Vempala · 2016
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Norms of random matrices: local and global problems
E. Rebrova and R. Vershynin · 2016
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Avoiding imposters and delinquents: Adversarial crowdsourcing and peer prediction
J. Steinhardt, G. Valiant, and M. Charikar · 2016
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