Traffic simulation provides interactive data for the optimization of tra...
Offline reinforcement learning (RL) tries to learn the near-optimal poli...
Traffic simulators act as an essential component in the operating and
pl...
The heavy traffic and related issues have always been concerns for moder...
Simulation of the real-world traffic can be used to help validate the
tr...
Historical features are important in ads click-through rate (CTR) predic...
Learning quickly is of great importance for machine intelligence deploye...
Recently, E-commerce platforms have extensive impacts on our human life....
Meta-learning has proven to be a powerful paradigm for transferring the
...
Modeling how human moves on the space is useful for policy-making in
tra...
Connecting consumers with relevant products is a very important problem ...
In order to efficiently learn with small amount of data on new tasks,
me...
Knowledge graphs (KGs) serve as useful resources for various natural lan...
Towards the challenging problem of semi-supervised node classification, ...
In the face of growing needs for water and energy, a fundamental
underst...
In order to learn quickly with few samples, meta-learning utilizes prior...
Traffic signal control is an emerging application scenario for reinforce...
Increasingly available city data and advanced learning techniques have
e...
With the increasing availability of traffic data and advance of deep
rei...
Cooperation is critical in multi-agent reinforcement learning (MARL). In...
Traffic signal control is an important and challenging real-world proble...
Spatial-temporal prediction is a fundamental problem for constructing sm...
Advances in sensor technology have enabled the collection of large-scale...
Given a large collection of urban datasets, how can we find their hidden...
Spatial-temporal prediction has many applications such as climate foreca...
Fisher score is one of the most widely used supervised feature selection...