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Title: ROBUST ESTIMATION VIA GENERATIVE ADVERSARIAL NETWORKS

Paper content:
Robust estimation under Huber's $\epsilon$-contamination model has become an important topic in statistics and theoretical computer science.
Rate-optimal procedures such as Tukey's median and other estimators based on statistical depth functions are impractical because of their computational intractability.
In this paper, we establish an intriguing connection between f-GANs and various depth functions through the lens of f-Learning.
Similar to the derivation of f-GAN, we show that these depth functions that lead to rate-optimal robust estimators can all be viewed as variational lower bounds of the total variation distance in the framework of f-Learning.
This connection opens the door of computing robust estimators using tools developed for training GANs.
In particular, we show that a JS-GAN that uses a neural network discriminator with at least one hidden layer is able to achieve the minimax rate of robust mean estimation under Huber's $\epsilon$-contamination model.
Interestingly, the hidden layers of the neural net structure in the discriminator class are shown to be necessary for robust estimation.
In the setting of Huber's -contamination model (Huber, 1964; 1965) , one has i.i.d observations X 1 , ..., X n ∼ (1 − )P θ + Q,and the goal is to estimate the model parameter θ.
Under the data generating process (1), each observation has a 1 − probability to be drawn from P θ and the other probability to be drawn from the contamination distribution Q. The presence of an unknown contamination distribution poses both statistical and computational challenges to the problem.
For example, consider a normal mean estimation problem with P θ = N (θ, I p ).
Due to the contamination of data, the sample average, which is optimal when = 0, can be arbitrarily far away from the true mean if Q charges a positive probability at infinity.
Moreover, even robust estimators such as coordinatewise median and geometric median are proved to be suboptimal under the setting of (1) (Chen et al., 2018; Diakonikolas et al., 2016a; Lai et al., 2016) .
The search for both statistically optimal and computationally feasible procedures has become a fundamental problem in areas including statistics and computer science.For the normal mean estimation problem, it has been shown in Chen et al. (2018) that the minimax rate with respect to the squared 2 loss is p n ∨ 2 , and is achieved by Tukey's median (Tukey, 1975) .
Despite the statistical optimality of Tukey's median, its computation is not tractable.
In fact, even an approximate algorithm takes O(e Cp ) in time BID1 Chan, 2004; Rousseeuw & Struyf, 1998) .Recent
developments in theoretical computer science are focused on the search of computationally tractable algorithms for estimating θ under Huber's -contamination model (1). The success
of the efforts started from two fundamental papers Diakonikolas et al. (2016a) ; Lai et al. (2016) , where two different but related computational strategies "iterative filtering" and "dimension halving" were proposed to robustly estimate the normal mean. These algorithms
can provably achieve the minimax rate p n ∨ 2 up to a poly-logarithmic factor in polynomial time. The main idea behind
the two methods is a critical fact that a good robust moment estimator can be certified efficiently by higher moments. This idea was later
further extended (Diakonikolas et al., 2017; Du et al., 2017; Diakonikolas et al., 2016b; 2018a; c; b; Kothari et al., 2018) to develop robust and computable procedures for various other problems.However, many of the computationally feasible procedures for robust mean estimation in the literature rely on the knowledge of covariance matrix and sometimes the knowledge of contamination proportion. Even though these assumptions
can be relaxed, nontrivial modifications of the algorithms are required for such extensions and statistical error rates may also be affected. Compared with these computationally
feasible procedures proposed in the recent literature for robust estimation, Tukey's median (9) and other depth-based estimators (Rousseeuw & Hubert, 1999; Mizera, 2002; Zhang, 2002; Mizera & Müller, 2004; Paindaveine & Van Bever, 2017) have some indispensable advantages in terms of their statistical properties. First, the depth-based estimators have
clear objective functions that can be interpreted from the perspective of projection pursuit (Mizera, 2002) . Second, the depth-based procedures are
adaptive to unknown nuisance parameters in the models such as covariance structures, contamination proportion, and error distributions (Chen et al., 2018; Gao, 2017) . Last but not least, Tukey's depth and
other depth functions are mostly designed for robust quantile estimation, while the recent advancements in the theoretical computer science literature are all focused on robust moments estimation. Although this is not an issue when it
comes to normal mean estimation, the difference is fundamental for robust estimation under general settings such as elliptical distributions where moments do not necessarily exist.Given the desirable statistical properties discussed above, this paper is focused on the development of computational strategies of depth-like procedures. Our key observation is that robust estimators
that are maximizers of depth functions, including halfspace depth, regression depth and covariance matrix depth, can all be derived under the framework of f -GAN (Nowozin et al., 2016) . As a result, these depth-based estimators can
be viewed as minimizers of variational lower bounds of the total variation distance between the empirical measure and the model distribution (Proposition 2.1). This observation allows us to leverage the recent
developments in the deep learning literature to compute these variational lower bounds through neural network approximations. Our theoretical results give insights on how to choose
appropriate neural network classes that lead to minimax optimal robust estimation under Huber's -contamination model. In particular, Theorem 3.1 and 3.2 characterize the networks
which can robustly estimate the Gaussian mean by TV-GAN and JS-GAN, respectively; Theorem 4.1 is an extension to robust location estimation under the class of elliptical distributions which includes Cauchy distribution whose mean does not exist. Numerical experiments in Section 5 are provided to show the
success of these GANs.

assistant: text: GANs are shown to provide us a new effective robust mean estimate against agnostic contaminations with both statistical optimality and practical tractability.