139 citations · 170 across the 18 of their papers we have counts for
4 papers · 1 filter
Perturbational Complexity by Distribution Mismatch: A Systematic Analysis of Reinforcement Learning in Reproducing Kernel Hilbert Space
Jihao Long, Jiequn Han
Most existing theoretical analysis of reinforcement learning (RL) is limited to the tabular setting or linear models due to the difficulty in dealing with function approximation in…
A spectral-based analysis of the separation between two-layer neural networks and linear methods
Lei Wu, Jihao Long
We propose a spectral-based approach to analyze how two-layer neural networks separate from linear methods in terms of approximating high-dimensional functions. We show that quanti…
A Class of Dimension-free Metrics for the Convergence of Empirical Measures
Jiequn Han, Ruimeng Hu, Jihao Long
This paper concerns the convergence of empirical measures in high dimensions. We propose a new class of probability metrics and show that under such metrics, the convergence is fre…
An Analysis of Reinforcement Learning in High Dimensions with Kernel and Neural Network Approximation
Jihao Long, Jiequn Han, Weinan E
Reinforcement learning (RL) algorithms based on high-dimensional function approximation have achieved tremendous empirical success in large-scale problems with an enormous number o…