12 citations · 40 across the 8 of their papers we have counts for
6 papers · 1 filter
Nonlinear Meta-Learning Can Guarantee Faster Rates
Dimitri Meunier, Zhu Li, Arthur Gretton +1
Many recent theoretical works on \emph{meta-learning} aim to achieve guarantees in leveraging similar representational structures from related tasks towards simplifying a target ta…
Optimal Rates for Regularized Conditional Mean Embedding Learning
Zhu Li, Dimitri Meunier, Mattes Mollenhauer +1
We address the consistency of a kernel ridge regression estimate of the conditional mean embedding (CME), which is an embedding of the conditional distribution of given int…
Towards an Understanding of Benign Overfitting in Neural Networks
Zhu Li, Zhi-Hua Zhou, Arthur Gretton
Modern machine learning models often employ a huge number of parameters and are typically optimized to have zero training loss; yet surprisingly, they possess near-optimal predicti…
Benign Overfitting and Noisy Features
Zhu Li, Weijie Su, Dino Sejdinovic
Modern machine learning often operates in the regime where the number of parameters is much higher than the number of data points, with zero training loss and yet good generalizati…
Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness
Zhu Li, Adrian Perez-Suay, Gustau Camps-Valls +1
Current adoption of machine learning in industrial, societal and economical activities has raised concerns about the fairness, equity and ethics of automated decisions. Predictive…
Towards A Unified Analysis of Random Fourier Features
Zhu Li, Jean-Francois Ton, Dino Oglic +1
Random Fourier features is a widely used, simple, and effective technique for scaling up kernel methods. The existing theoretical analysis of the approach, however, remains focused…