83 citations · 237 across the 22 of their papers we have counts for
5 papers · 2 filters
Locally Smoothed Gaussian Process Regression
Davit Gogolashvili, Bogdan Kozyrskiy, Maurizio Filippone
We develop a novel framework to accelerate Gaussian process regression (GPR). In particular, we consider localization kernels at each data point to down-weigh the contributions fro…
How Much is Enough? A Study on Diffusion Times in Score-based Generative Models
Giulio Franzese, Simone Rossi, Lixuan Yang +4
Score-based diffusion models are a class of generative models whose dynamics is described by stochastic differential equations that map noise into data. While recent works have sta…
Local Random Feature Approximations of the Gaussian Kernel
Jonas Wacker, Maurizio Filippone
A fundamental drawback of kernel-based statistical models is their limited scalability to large data sets, which requires resorting to approximations. In this work, we focus on the…
Complex-to-Real Sketches for Tensor Products with Applications to the Polynomial Kernel
Jonas Wacker, Ruben Ohana, Maurizio Filippone
Randomized sketches of a tensor product of vectors follow a tradeoff between statistical efficiency and computational acceleration. Commonly used approaches avoid computing the…
Improved Random Features for Dot Product Kernels
Jonas Wacker, Motonobu Kanagawa, Maurizio Filippone
Dot product kernels, such as polynomial and exponential (softmax) kernels, are among the most widely used kernels in machine learning, as they enable modeling the interactions betw…