20 citations · 51 across the 9 of their papers we have counts for
12 papers · 1 filter
Feature Cross Search via Submodular Optimization
Lin Chen, Hossein Esfandiari, Gang Fu +2
In this paper, we study feature cross search as a fundamental primitive in feature engineering. The importance of feature cross search especially for the linear model has been know…
Infinite-Horizon Offline Reinforcement Learning with Linear Function Approximation: Curse of Dimensionality and Algorithm
Lin Chen, Bruno Scherrer, Peter L. Bartlett
In this paper, we investigate the sample complexity of policy evaluation in infinite-horizon offline reinforcement learning (also known as the off-policy evaluation problem) with l…
Deep Neural Tangent Kernel and Laplace Kernel Have the Same RKHS
Lin Chen, Sheng Xu
We prove that the reproducing kernel Hilbert spaces (RKHS) of a deep neural tangent kernel and the Laplace kernel include the same set of functions, when both kernels are restricte…
The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization
Yifei Min, Lin Chen, Amin Karbasi
Adversarial training has shown its ability in producing models that are robust to perturbations on the input data, but usually at the expense of decrease in the standard accuracy.…
More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Models
Lin Chen, Yifei Min, Mingrui Zhang +1
Despite remarkable success in practice, modern machine learning models have been found to be susceptible to adversarial attacks that make human-imperceptible perturbations to the d…
Online Continuous Submodular Maximization: From Full-Information to Bandit Feedback
Mingrui Zhang, Lin Chen, Hamed Hassani +1
In this paper, we propose three online algorithms for submodular maximisation. The first one, Mono-Frank-Wolfe, reduces the number of per-function gradient evaluations from $T^{1/2…