2 citations · 6 across the 6 of their papers we have counts for
5 papers · 1 filter
A Variational Analysis of Kernel Learning with Learnable Linear Transformations
Yang Li, Feng Ruan
The classical kernel ridge regression problem aims to find the best fit for the output as a function of the input data , with a fixed choice of regularizatio…
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…
Nonparametric Modern Hopfield Models
Jerry Yao-Chieh Hu, Bo-Yu Chen, Dennis Wu +2
We present a nonparametric interpretation for deep learning compatible modern Hopfield models and utilize this new perspective to debut efficient variants. Our key contribution ste…
On the Self-Penalization Phenomenon in Feature Selection
Michael I. Jordan, Keli Liu, Feng Ruan
We describe an implicit sparsity-inducing mechanism based on minimization over a family of kernels: \begin{equation*} \min_{β, f}~\widehat{\mathbb{E}}[L(Y, f(β^{1/q} \odot X)] + λ_…
Is Temporal Difference Learning Optimal? An Instance-Dependent Analysis
Koulik Khamaru, Ashwin Pananjady, Feng Ruan +2
We address the problem of policy evaluation in discounted Markov decision processes, and provide instance-dependent guarantees on the -error under a generative model.…