Fetching the paper…
Reading the bibliography…
Robust estimation under Huber's $\epsilon$-contamination model has become an important topic in statistics and theoretical computer science.
Eine informationstheoretische ungleichung und ihre anwendung auf beweis der ergodizitaet von markoffschen ketten
Imre Csiszár · 1964
Earlier work this paper cites.
Robust estimation of a location parameter
Peter J Huber · 1964
Earlier work this paper cites.
A robust version of the probability ratio test
Peter J Huber · 1965
Earlier work this paper cites.
A general class of coefficients of divergence of one distribution from another
Syed Mumtaz Ali and Samuel D Silvey · 1966
Earlier work this paper cites.
Mathematics and the picturing of data
John W Tukey · 1975
Earlier work this paper cites.
Rates of convergence of minimum distance estimators and kolmogorov’s entropy
Yannis G Yatracos · 1985
Earlier work this paper cites.
On the method of bounded differences
Colin McDiarmid · 1989
Earlier work this paper cites.
Geometrizing rates of convergence, iii
David L Donoho and Richard C Liu · 1991
Earlier work this paper cites.
Breakdown properties of location estimates based on halfspace depth and projected outlyingness
David L Donoho, Miriam Gasko, et al · 1992
Earlier work this paper cites.
Weak convergence
Aad W Van Der Vaart and Jon A Wellner · 1996
Earlier work this paper cites.
For valid generalization the size of the weights is more important than the size of the network
Peter L Bartlett · 1997
Earlier work this paper cites.
Computing location depth and regression depth in higher dimensions
Peter J Rousseeuw and Anja Struyf · 1998
Earlier work this paper cites.
Multivariate analysis by data depth: descriptive statistics, graphics and inference,(with discussion and a rejoinder by liu and singh)
Regina Y Liu, Jesse M Parelius, and Kesar Singh · 1999
Earlier work this paper cites.
Regression depth
Peter J Rousseeuw and Mia Hubert · 1999
Earlier work this paper cites.
Efficient algorithms for maximum regression depth
Marc van Kreveld, Joseph SB Mitchell, Peter Rousseeuw, Micha Sharir, Jack Snoeyink, and Bettina Speckmann · 1999
Earlier work this paper cites.
Regression depth and center points
Nina Amenta, Marshall Bern, David Eppstein, and S-H Teng · 2000
Earlier work this paper cites.
General notions of statistical depth function
Yijun Zuo and Robert Serfling · 2000
Earlier work this paper cites.
Rademacher and gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2002
Earlier work this paper cites.
On depth and deep points: a calculus
Ivan Mizera · 2002
Earlier work this paper cites.
Some extensions of tukey’s depth function
Jian Zhang · 2002
Cited alongside, same era.
An optimal randomized algorithm for maximum tukey depth
Timothy M Chan · 2004
Cited alongside, same era.
Location–scale depth
Ivan Mizera and Christine H Müller · 2004
Cited alongside, same era.
Theory of point estimation
Erich L Lehmann and George Casella · 2006
Cited alongside, same era.
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
Cited alongside, same era.
Estimating divergence functionals and the likelihood ratio by convex risk minimization
XuanLong Nguyen, Martin J Wainwright, and Michael I Jordan · 2010
Cited alongside, same era.
A new method for estimation and model selection: ρ \rho -estimation
Yannick Baraud, Lucien Birgé, and Mathieu Sart · 2017
Later among the works it cites.
Being robust (in high dimensions) can be practical
Ilias Diakonikolas, Gautam Kamath, Daniel M Kane, Jerry Li, Ankur Moitra, and Alistair Stewart · 2017
Later among the works it cites.
Computationally efficient robust estimation of sparse functionals
Simon S Du, Sivaraman Balakrishnan, and Aarti Singh · 2017
Later among the works it cites.
Symmetric Multivariate and Related Distributions: 0
Kai Wang Fang · 2017
Later among the works it cites.
Understanding gans: the lqg setting
Soheil Feizi, Changho Suh, Fei Xia, and David Tse · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Luc Devroye and Gábor Lugosi · 2012
Cited alongside, same era.
Convergence of stochastic processes
David Pollard · 2012
Cited alongside, same era.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Cited alongside, same era.
Training generative neural networks via maximum mean discrepancy optimization
Gintare Karolina Dziugaite, Daniel M Roy, and Zoubin Ghahramani · 2015
Cited alongside, same era.
Generative moment matching networks
Yujia Li, Kevin Swersky, and Rich Zemel · 2015
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Cited alongside, same era.
Robust regression via mutivariate regression depth
Chao Gao · 2017
Later among the works it cites.
How well can generative adversarial networks (gan) learn densities: A nonparametric view
Tengyuan Liang · 2017
Later among the works it cites.
Approximation and convergence properties of generative adversarial learning
Shuang Liu, Olivier Bousquet, and Kamalika Chaudhuri · 2017
Later among the works it cites.
Are gans created equal? a large-scale study
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet · 2017
Later among the works it cites.
Mcgan: Mean and covariance feature matching gan
Youssef Mroueh, Tom Sercu, and Vaibhava Goel · 2017
Later among the works it cites.
Halfspace depths for scatter, concentration and shape matrices
Davy Paindaveine and Germain Van Bever · 2017
Later among the works it cites.
Lecture notes on information theory
Yury Polyanskiy and Yihong Wu · 2017
Later among the works it cites.
Approximability of discriminators implies diversity in gans
Yu Bai, Tengyu Ma, and Andrej Risteski · 2018
Closest in time.
Mikołaj Bińkowski, Dougal J Sutherland, Michael Arbel, and Arthur Gretton · 2018
Closest in time.
Robust covariance and scatter matrix estimation under huber’s contamination model
Mengjie Chen, Chao Gao, and Zhao Ren · 2018
Closest in time.
Robust moment estimation and improved clustering via sum of squares
Pravesh K Kothari, Jacob Steinhardt, and David Steurer · 2018
Closest in time.
The gan landscape: Losses, architectures, regularization, and normalization
Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, and Sylvain Gelly · 2018
Closest in time.
Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Closest in time.
On general notions of depth for regression
Yijun Zuo · 2018
Closest in time.