I don’t know if there are good resources about it (I would guess you could find some papers describing this approach). The main idea would be to not only train the model with the reconstruction loss, but also to force it to learn to classify the samples. Since you are training the model end2end this would also influence the latent space in the bottleneck of the model, so that it has to encode the “classification information” as well and which might then benefit the downstream LGBM model.