4 papers
There is No VAE: End-to-End Pixel-Space Generative Modeling via Self-Supervised Pre-training
Jiachen Lei, Keli Liu, Julius Berner +4
Pixel-space generative models are often more difficult to train and generally underperform compared to their latent-space counterparts, leaving a persistent performance and efficie…
A Compositional Kernel Model for Feature Learning
Feng Ruan, Keli Liu, Michael Jordan
We study a compositional variant of kernel ridge regression in which the predictor is applied to a coordinate-wise reweighting of the inputs. Formulated as a variational problem, t…
On the Limitation of Kernel Dependence Maximization for Feature Selection
Keli Liu, Feng Ruan
A simple and intuitive method for feature selection consists of choosing the feature subset that maximizes a nonparametric measure of dependence between the response and the featur…
A Theory of Feature Learning in Kernel Models
Yunlu Chen, Yang Li, Keli Liu +1
We study feature learning in a compositional variant of kernel ridge regression in which the predictor is applied to a learnable linear transformation of the input. When the respon…