The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> D. Biased inspection results <s> We present a series of new theoretical, algorithmic, and empirical results for domain adaptation and sample bias correction in regression. We prove that the discrepancy is a distance for the squared loss when the hypothesis set is the reproducing kernel Hilbert space induced by a universal kernel such as the Gaussian kernel. We give new pointwise loss guarantees based on the discrepancy of the empirical source and target distributions for the general class of kernel-based regularization algorithms. These bounds have a simpler form than previous results and hold for a broader class of convex loss functions not necessarily differentiable, including L"q losses and the hinge loss. We also give finer bounds based on the discrepancy and a weighted feature discrepancy parameter. We extend the discrepancy minimization adaptation algorithm to the more significant case where kernels are used and show that the problem can be cast as an SDP similar to the one in the feature space. We also show that techniques from smooth optimization can be used to derive an efficient algorithm for solving such SDPs even for very high-dimensional feature spaces and large samples. We have implemented this algorithm and report the results of experiments both with artificial and real-world data sets demonstrating its benefits both for general scenario of adaptation and the more specific scenario of sample bias correction. Our results show that it can scale to large data sets of tens of thousands or more points and demonstrate its performance improvement benefits. <s> BIB001 </s> The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> D. Biased inspection results <s> Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections. Accurate prediction of NTLs for customers using machine learning is therefore crucial. To date, related research largely ignore that the two classes of regular and non-regular customers are highly imbalanced, that NTL proportions may change and mostly consider small data sets, often not allowing to deploy the results in production. In this paper, we present a comprehensive approach to assess three NTL detection models for different NTL proportions in large real world data sets of 100Ks of customers: Boolean rules, fuzzy logic and Support Vector Machine. This work has resulted in appreciable results that are about to be deployed in a leading industry solution. We believe that the considerations and observations made in this contribution are necessary for future smart meter research in order to report their effectiveness on imbalanced and large real world data sets. <s> BIB002
In statistics, samples of data must represent the overall population in order to make valid conclusions. This is a longstanding issue in statistics and therefore in machine learning, too, as discussed in  . In the preliminary work of BIB002 we noticed that the sample of previously inspected customers may not be representative of all customers. One reason is, for example, that electricity suppliers previously focused on certain neighborhoods for inspections. NTL classifiers trained on biased inspection data are likely to be biased, too. To the best of our knowledge, this topic has not been addressed in the literature on NTL detection. Bias correction has initially been addressed in the field of computational learning theory BIB001 . We believe that it can be promising to explore in this direction. For example, one promising approach may be resampling inspection data in order to be representative in terms of location and type of customer.