Robustness against manipulations in machine learning

Abstract

A method comprising: receiving observed data points each comprising a vector of feature values, wherein for each data point, the respective feature values are values of different features of a feature vector. Each observed data point represents a respective observation of a ground truth as observed in the form of the respective values of the feature vector. The method further comprises learning parameters of a machine-learning model based on the observed data points. The machine-learning model comprises one or more statistical models arranged to model a causal relationship between the feature vector and a latent vector, a classification, and a manipulation vector. The manipulation vector represents an effect of potential manipulations occurring between the ground truth and the observation thereof as observed via the feature vector. The learning comprises learning parameters of the one or more statistical models to map between the feature vector, latent vector, classification and manipulation vector.

1 . A computer-implemented method of machine learning, the method comprising:
 receiving a plurality of observed data points each comprising a respective vector of feature values, wherein for each observed data point, the respective feature values are values of a plurality of different features of a feature vector, and each observed data point represents a respective observation of a ground truth as observed in the form of the respective values of the feature vector; and   learning parameters of a machine-learning model based on the observed data points, wherein the machine-learning model comprises one or more statistical models arranged to model a causal relationship between the feature vector and a latent vector, a classification, and a manipulation vector, the manipulation vector representing an effect of potential manipulations occurring between the ground truth and the observation of the group truth as observed via said feature vector, wherein the learning comprises learning parameters of the one or more statistical models to map between the feature vector, latent vector, classification and manipulation vector.