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We consider a general statistical learning problem where an unknown fraction of the training data is corrupted. We develop a robust learning method that only requires specifying an upper bound on the corrupted data fraction. The method minimizes a risk function defined by a non-parametric distribution with unknown probability weights. We derive and analyse the optimal weights and show how they provide robustness against corrupted data. Furthermore, we give a computationally efficient coordinate descent algorithm to solve the risk minimization problem. We demonstrate the wide range applicability of the method, including regression, classification, unsupervised learning and classic parameter estimation, with state-of-the-art performance.

We address the problem of timing-based localization in wireless networks, when an unknown fraction of data is corrupted by non-ideal propagation conditions. While timing-based techniques can enable accurate localization, they are sensitive to corrupted data. We develop a robust method that is applicable to a range of localization techniques, including time-of-arrival, time-difference-of-arrival and time-difference in schedule-based transmissions. The method is distribution-free, is computationally efficient and requires only an upper bound on the fraction of corrupted data, thus obviating distributional assumptions on the corrupting noise. The robustness of the method is demonstrated in numerical experiments.

We address the problem of inferring the causal effect of an exposure on an outcome across space, using observational data. The data is possibly subject to unmeasured confounding variables which, in a standard approach, must be adjusted for by estimating a nuisance function. Here we develop a method that eliminates the nuisance function, while mitigating the resulting errors-in-variables. The result is a robust and accurate inference method for spatially varying heterogeneous causal effects. The properties of the method are demonstrated on synthetic as well as real data from Germany and the US.

We address the problem of learning a decision policy from observational data of past decisions in contexts with features and associated outcomes. The past policy maybe unknown and in safety-critical applications, such as medical decision support, it is of interest to learn robust policies that reduce the risk of outcomes with high costs. In this paper, we develop a method for learning policies that reduce tails of the cost distribution at a specified level and, moreover, provide a statistically valid bound on the cost of each decision. These properties are valid under finite samples -- even in scenarios with uneven or no overlap between features for different decisions in the observed data -- by building on recent results in conformal prediction. The performance and statistical properties of the proposed method are illustrated using both real and synthetic data. 

We consider the problem of learning decision parameters from data obtained in different contexts. When future context information is inaccessible, we consider the resulting (i) worst-case and (ii) overall out-of-sample performance of the learned parameters. We propose a robust approach that trades off these two performance criteria based on the partial information obtained about the unknown context distribution. The proposed method overcomes the overly conservative nature of the minimax method, while robustifying the empirical risk minimization method in a statistically motivated manner. We illustrate the performance of the method in a classification task.

A spatial point process can be characterized by an intensity function which predicts the number of events that occur across space. In this paper, we develop a method to infer predictive intensity intervals by learning a spatial model using a regularized criterion. We prove that the proposed method exhibits out-of-sample prediction performance guarantees which, unlike standard estimators, are valid even when the spatial model is misspecified. The method is demonstrated using synthetic as well as real spatial data.

We consider the online learning of linear smoother predictors based on a covariance model of the outcomes. To control its degrees of freedom in an appropriate manner, the covariance model parameters are often learned using cross-validation or maximum-likelihood techniques. However, neither technique is suitable when training data arrives in a streaming fashion. Here we consider a covariance-fitting method to learn the model parameters, initially used  in spectral estimation. We show that this results in a computation efficient online learning method in which the resulting predictor can be updated sequentially. We prove that, with high probability, its out-of-sample error approaches the minimum achievable level at root-n rate. Moreover, we show that the resulting predictor enjoys two different robustness properties. First, it minimizes the out-of-sample error with respect to the least favourable distribution within a given Wasserstein distance from the empirical distribution. Second, it is robust against errors in the covariate training data. We illustrate the performance of the proposed method in a numerical experiment.