The vast majority of machine learning models are trained using a static dataset; that is, the dataset is fixed and does not change over time. This can be a problem when dealing with time-sensitive data, as the model may not be able to adapt to changes in the underlying data distribution. Active learning is a methodology that addresses this issue by allowing the model to interactively query the user for labels, which can be used to expand the training dataset. This can be done in a number of ways, such as through reinforcement learning or Bayesian optimization.
Active learning has been shown to be effective in a variety of settings, including text classification, image classification, and cancer detection. It is also well suited for problems where the data is “noisy” or contains a lot of missing information.
There are a few different approaches to active learning. One approach is to use a reinforcement learning algorithm, which essentially treats the labeling process as a game. The aim is to learn a policy that will minimize the number of queries needed to reach a desired accuracy level. Another approach is to use Bayesian optimization, which tries to optimize a function by iteratively selecting the point that is most likely to result in the optimum value.
A recent trend in machine learning is the use of large language models (LLMs). These models are trained on a large amount of text data and can be used for a variety of tasks, such as text generation, machine translation, and question answering. LLMs have been shown to be particularly effective for active learning tasks. This is because they are able to quickly adapt to changes in the underlying data distribution and can generate high-quality labels.
There are a few different ways to solve the active learning problem. One approach is to use a reinforcement learning algorithm, which essentially treats the labeling process as a game. The aim is to learn a policy that will minimize the number of queries needed to reach a desired accuracy level. Another approach is to use Bayesian optimization, which tries to optimize a function by iteratively selecting the point that is most likely to result in the optimum value. A recent trend in machine learning is the use of large language models (LLMs). These models are trained on a large amount of text data and can be used for a variety of tasks, such as text generation, machine translation, and question answering. LLMs have been shown to be particularly effective for active learning tasks. This is because they are able to quickly adapt to changes in the underlying data distribution and can generate high-quality labels.
References:
https://en.wikipedia.org/wiki/Active_learning_(machine_learning)
https://towardsdatascience.com/reinforcement-learning-for-active-learning-f0abd71a5f84
https://towardsdatascience.com/bayesian-optimization-for-active-learning-d88455ba3144
https://arxiv.org/abs/1911.03548