Atlas' classifier adopts an inducing point approach, in which the latent variables are augmented with additional input (output) data, known as inducing inputs (points). Our strategy follows closely to the one detailed in Hensman et al., where a bound on the marginal likelihood for classification problems is derived. This bound is optimized by adjusting the hyperparameters of the GP kernel, parameters of the multivariate normal variational distribution, and the inducing inputs/points simultaneously using stochastic gradient descent. The reader is referred to previous work for additional details. GP classification is implemented using the GpyTorch library, and has the added benefit of scaling more favorably with |D n c | than exact GP inference.