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Experimental Setup
We provide an extended version of the Experimental Setup from Section 5 below. Linear Model This domain involves learning a linear model when the underlying mapping between features and predictions is cubic. Concretely, the aim is to choose the top B =1 out of N = 50 resources using a linear model. The fact that the features can be seen as 1-dimensional allows us to visualize the learned models (as seen in Figure 4). Predict: Given a feature xn U[0,1], use a linear model to predict the utility ˆyof choosing resource n, where the true utility is given by yn = 10x3n 6.5xn.