Sparse Variational Student-t Processes for Heavy-tailed Modeling

ArXi:2408.06699v2 Announce Type: replace-cross 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. Studentt processes 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 Student-t Processes (SVTP), the first principled framework that extends the sparse inducing point method to the Student-t process.