Conventional machine learning studies generally assume close-environment scenarios
where important factors of the learning process hold invariant. With the great success of …

The problem of concept drift has gained a lot of attention in recent years. This aspect is key
in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks …

Today's data centers rely more heavily on machine learning (ML) in their deployed systems.
However, these systems are vulnerable to the data drift problem, that is, a mismatch …

The standard supervised learning paradigm works effectively when training data shares the
same distribution as the upcoming testing samples. However, this stationary assumption is …

In many real-world scenarios, we often deal with streaming data that is sequentially
collected over time. Due to the non-stationary nature of the environment, the streaming data …

When learning from streaming data, a change in the data distribution, also known as
concept drift, can render a previously-learned model inaccurate and require training a new …

In many real-world applications, data are collected in the form of a stream, whose feature
space can evolve over time. For instance, in the environmental monitoring task, features can …

There are complaints about current machine learning techniques such as the requirement of
a huge amount of training data and proficient training skills, the difficulty of continual …

Data distributions in streaming environments are usually not stationary. In order to maintain
a high predictive quality at all times, online learning models need to adapt to distributional …

Accurate short-term wind speed forecasting is essential for wind power system scheduling
optimization and profit maximization. However, the distribution of wind speed evolves over …