Ascalculatingthese residuals requires out-of-sample inference, we fit the models and half of the data and evaluate on the other half, before switching the training and testing sets and training/inferring again. We used the Adam optimizer [4] and PyTorch's exponential learning rate scheduler with gammasetto0.99.

There has been increasing interest in developing multi-label classification methods for E-MLC [17,19,18,13,7,28,15,4,24].

For the experiments described in Section 3.5 of the main paper, all methods which required a Bayesian These residuals are obtained by first training a separate classifier per each class, and then calculating the residual as the error between the predicted and ground truth class. Training Hyperparameters For each method, we used a batch size of 128 and a learning rate of 0.001. Each method was trained until convergence for 200 epochs. To validate that our "non-noisy" class conditioning approach is RBCC, and the class ordering implies that each class is predicted before its parent classes. Results are shown in Figure 1.

Recurrent Classifier Chains (RCCs), a recurrent neural network extension of ensemble-based classifier chains, are the state-of-the-art exact multi-label classification method for maximizing subset accuracy.