Fetching the paper…
Reading the bibliography…
In this paper, we study a classification problem in which sample labels are randomly corrupted.
Defense Technical Information Center, 1977
H. Moore and N. P. S. M. CALIF., Robust Regression Using Maximum-Likelihood Weighting and Assuming Cauchy-Distributed Random Error · 1977
Earlier work this paper cites.
L. G. Valiant, “A theory of the learnable,” Communications of the ACM
1984
Earlier work this paper cites.
D. Angluin and P. Laird, “Learning from noisy examples,” Machine Learning
1988
Earlier work this paper cites.
M. Kearns and M. Li, “Learning in the presence of malicious errors,” SIAM Journal on Computing
1993
Earlier work this paper cites.
T. Bylander, “Learning linear threshold functions in the presence of classification noise,” in COLT
1994
Earlier work this paper cites.
J. A. Aslam and S. E. Decatur, “On the sample complexity of noise-tolerant learning,” Information Processing Letters
1996
Earlier work this paper cites.
E. Cohen, “Learning noisy perceptrons by a perceptron in polynomial time,” in FOCS
1997
Earlier work this paper cites.
M. Kearns, “Efficient noise-tolerant learning from statistical queries,” Journal of the ACM
1998
Earlier work this paper cites.
A. Blum, A. Frieze, R. Kannan, and S. Vempala, “A polynomial-time algorithm for learning noisy linear threshold functions,” Algorithmica
1998
Earlier work this paper cites.
N. Cesa-Bianchi, E. Dichterman, P. Fischer, E. Shamir, and H. U. Simon, “Sample-efficient strategies for learning in the presence of noise,” Journal of the ACM
1999
Earlier work this paper cites.
springer, 2000
V. Vapnik, The Nature of Statistical Learning Theory · 2000
Earlier work this paper cites.
N. D. Lawrence and B. Schölkopf, “Estimating a kernel Fisher discriminant in the presence of label noise,” in ICML
2001
Earlier work this paper cites.
N. H. Bshouty, N. Eiron, and E. Kushilevitz, “PAC learning with nasty noise,” Theoretical Computer Science
2002
Earlier work this paper cites.
I. Steinwart, “Support vector machines are universally consistent,” Journal of Complexity
2002
Earlier work this paper cites.
P. L. Bartlett and S. Mendelson, “Rademacher and Gaussian complexities: Risk bounds and structural results,” Journal of Machine Learning Research
2003
Earlier work this paper cites.
H. Zou and T. Hastie, “Regularization and variable selection via the elastic net,” Journal of the Royal Statistical Society: Series B (Statistical Methodology)
2005
Earlier work this paper cites.
Y. Nesterov, “Smooth minimization of non-smooth functions,” Mathematical programming
2005
Earlier work this paper cites.
P. L. Bartlett, O. Bousquet, and S. Mendelson, “Local Rademacher complexities,” The Annals of Statistics
2005
Earlier work this paper cites.
P. L. Bartlett, M. I. Jordan, and J. D. McAuliffe, “Convexity, classification, and risk bounds,” Journal of the American Statistical Association
2006
Cited alongside, same era.
M. Belkin, P. Niyogi, and V. Sindhwani, “Manifold regularization: A geometric framework for learning from labeled and unlabeled examples,” Journal of Machine Learning Research
2006
Cited alongside, same era.
J. Huang, A. Gretton, K. M. Borgwardt, B. Schölkopf, and A. J. Smola, “Correcting sample selection bias by unlabeled data,” in NIPS
2006
Cited alongside, same era.
G. Stempfel and L. Ralaivola, “Learning kernel perceptrons on noisy data using random projections,” in ALT
2007
Cited alongside, same era.
R. Khardon and G. Wachman, “Noise tolerant variants of the perceptron algorithm,” Journal of Machine Learning Research
2007
Cited alongside, same era.
B. Biggio, B. Nelson, and P. Laskov, “Support vector machines under adversarial label noise,” in ACML
2011
Later among the works it cites.
P. M. Long and R. A. Servedio, “Learning large-margin halfspaces with more malicious noise,” in NIPS
2011
Later among the works it cites.
N. Cesa-Bianchi, S. Shalev-Shwartz, and O. Shamir, “Online learning of noisy data,” IEEE Transactions on Information Theory
2011
Later among the works it cites.
R. He, W. Zheng, and B. Hu, “Maximum correntropy criterion for robust face recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2011
Later among the works it cites.
M. Yamada, T. Suzuki, T. Kanamori, H. Hachiya, and M. Sugiyama, “Relative density-ratio estimation for robust distribution comparison,” in NIPS
2011
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
W. Liu, P. P. Pokharel, and J. C. Príncipe, “Correntropy: properties and applications in non-gaussian signal processing,” IEEE Transactions on Signal Processing
2007
Cited alongside, same era.
A. R. Klivans, P. M. Long, and R. A. Servedio, “Learning halfspaces with malicious noise,” Journal of Machine Learning Research
2009
Cited alongside, same era.
cambridge university press, 2009
M. Anthony and P. L. Bartlett, Neural Network Learning: Theoretical Foundations · 2009
Cited alongside, same era.
A. Gretton, A. Smola, J. Huang, M. Schmittfull, K. Borgwardt, and B. Schölkopf, “Covariate shift by kernel mean matching,” in Dataset shift in machine learning
2009
Cited alongside, same era.
T. Kanamori, S. Hido, and M. Sugiyama, “A least-squares approach to direct importance estimation,” Journal of Machine Learning Research
2009
Cited alongside, same era.
R. Nock and F. Nielsen, “Bregman divergences and surrogates for learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2009
Cited alongside, same era.
T. K. Pong, P. Tseng, S. Ji, and J. Ye, “Trace norm regularization: reformulations, algorithms, and multi-task learning,” SIAM Journal on Optimization
2010
Cited alongside, same era.
Q. Sun, R. Chattopadhyay, S. Panchanathan, and J. Ye, “A two-stage weighting framework for multi-source domain adaptation,” in NIPS
2011
Later among the works it cites.
T. Yang, M. Mahdavi, R. Jin, L. Zhang, and Y. Zhou, “Multiple kernel learning from noisy labels by stochastic programming,” in ICML
2012
Later among the works it cites.
C. Scott, “Calibrated asymmetric surrogate losses,” Electronic Journal of Statistics
2012
Later among the works it cites.
MIT Press, 2012
M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of Machine Learning · 2012
Later among the works it cites.
2012
Later among the works it cites.
N. Manwani and P. Sastry, “Noise tolerance under risk minimization,” IEEE Transactions on Cybernetics
2013
Later among the works it cites.
N. Natarajan, I. Dhillon, P. Ravikumar, and A. Tewari, “Learning with noisy labels,” in NIPS
2013
Later among the works it cites.
P. Gong, C. Zhang, Z. Lu, J. Huang, and J. Ye, “A general iterative shrinkage and thresholding algorithm for non-convex regularized optimization problems,” in ICML
2013
Later among the works it cites.
C. Scott, G. Blanchard, and G. Handy, “Classification with asymmetric label noise: Consistency and maximal denoising,” in COLT
2013
Later among the works it cites.
2013
Later among the works it cites.
B. Frénay and M. Verleysen, “Classification in the presence of label noise: A survey,” IEEE Transactions on Neural Networks and Learning Systems
2014
Closest in time.
C. Scott, “A rate of convergence for mixture proportion estimation, with application to learning from noisy labels,” in AISTATS
2015
Closest in time.