Sparse Variational Student-t Processes for Heavy-Tailed Modeling

The Gaussian process (GP) is a powerful tool for nonparametric modeling, but its sensitivity to outliers limits its applicability to data distributions with heavy tails. Student-t processes (TPs) offer a robust alternative for heavy-tail modeling, but they lack the scalable developments of the GP to large datasets necessary for practical applications. We present sparse variational TPs (SVTPs), the first principled framework that extends the sparse inducing point method to the TP. We develop two