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Understanding how environmental characteristics affect bio-diversitypatterns, from individual species to communities of species, is critical formitigating effects of global change. A central goal for conservation planningand monitoring is the ability to accurately predict the occurrence of speciescommunities and how these communities change over space and time. This in turnleads to a challenging and long-standing problem in the field of computerscience - how to perform ac-curate multi-label classification with hundreds oflabels? The key challenge of this problem is its exponential-sized output spacewith regards to the number of labels to be predicted.Therefore, it is essentialto facilitate the learning process by exploiting correlations (or dependency)among labels. Previous methods mostly focus on modelling the correlation onlabel pairs; however, complex relations between real-world objects often gobeyond second order. In this paper, we pro-pose a novel framework formulti-label classification, High-order Tie-in Variational Autoencoder(HOT-VAE), which per-forms adaptive high-order label correlation learning. Weexperimentally verify that our model outperforms the existing state-of-the-artapproaches on a bird distribution dataset on both conventional F1 scores and avariety of ecological metrics. To show our method is general, we also performempirical analysis on seven other public real-world datasets in severalapplication domains, and Hot-VAE exhibits superior performance to previousmethods.

HOT-VAE: Learning High-Order Label Correlation for Multi-Label Classification via Attention-Based Variational Autoencoders