128 citations · 129 across the 3 of their papers we have counts for
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cs.LG2022
Rate-matching the regret lower-bound in the linear quadratic regulator with unknown dynamics
Feicheng Wang, Lucas Janson
The theory of reinforcement learning currently suffers from a mismatch between its empirical performance and the theoretical characterization of its performance, with consequences…
cs.LG2020★ 1 cited
Exact Asymptotics for Linear Quadratic Adaptive Control
Feicheng Wang, Lucas Janson
Recent progress in reinforcement learning has led to remarkable performance in a range of applications, but its deployment in high-stakes settings remains quite rare. One reason is…
cs.LG2017★ 128 cited
The Expressive Power of Neural Networks: A View from the Width
Zhou Lu, Hongming Pu, Feicheng Wang +2
The expressive power of neural networks is important for understanding deep learning. Most existing works consider this problem from the view of the depth of a network. In this pap…