Traffic prediction is a crucial topic because of its broad scope of
appl...
For modern gradient-based optimization, a developmental landmark is
Nest...
The high-resolution differential equation framework has been proven to b...
The emergence of reinforcement learning (RL) methods in traffic signal
c...
In this paper, we revisit the class of iterative shrinkage-thresholding
...
Nesterov's accelerated gradient descent (NAG) is one of the milestones i...
This paper introduces a library for cross-simulator comparison of
reinfo...
For first-order smooth optimization, the research on the acceleration
ph...
In the history of first-order algorithms, Nesterov's accelerated gradien...
Modeling how network-level traffic flow changes in the urban environment...
In this paper, we propose a sampling algorithm based on statistical mach...
There is an increasing trend of research in mediation analysis for survi...
Following the same routine as [SSJ20], we continue to present the theore...
The learning rate is perhaps the single most important parameter in the
...
We study first-order optimization methods obtained by discretizing ordin...
Gradient-based optimization algorithms can be studied from the perspecti...
We propose some algorithms to find local minima in nonconvex optimizatio...