Bayesian Optimization (BO) is an effective framework for black-box optimization.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Existing regression models tend to fall short in both accuracy and uncertainty estimation when the label distribution is imbalanced. In this paper, we propose a probabilistic deep learning model, dubbed variational imbalanced regression (VIR), which not only performs well in imbalanced regression but naturally produces reasonable uncertainty estimation as a byproduct. Different from typical variational autoencoders assuming I.I.D. representations (a data point's representation is not directly affected by other data points), our VIR borrows data with similar regression labels to compute the latent representation's vari-ational distribution; furthermore, different from deterministic regression models producing point estimates, VIR predicts the entire normal-inverse-gamma distributions and modulates the associated conjugate distributions to impose probabilistic reweighting on the imbalanced data, thereby providing better uncertainty estimation.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

The relative performance gain for Fig.1 c) is In Tab. 6, we show FPS(F) FPS(E) of various feature fusion models with the varied set sizeN. Notethatmethodswithout intra-set relationships, PFE [11] and CFAN [3], are computationally very fast and require little memory. Incontrast, the maximum set sizeN for RSA [7] is384 because the intra-set attention with the feature map is a memory-intensivemodule. In other words, it is the mean of the row-wise entropy of the normalized assignment map. Lower entropy value tells you that the cluster features are deviating from a simple average of all samples.