Group online adaptive learning

Abstract

Sharing information among multiple learning agents can accelerate learning. It could be particularly useful if learners operate in continuously changing environments, because a learner could benefit from previous experience of another learner to adapt to their new environment. Such group-adaptive learning has numerous applications, from predicting financial time-series, through content recommendation systems, to visual understanding for adaptive autonomous agents. Here we address the problem in the context of online adaptive learning. We formally define the learning settings of Group Online Adaptive Learning and derive an algorithm named Shared Online Adaptive Learning (SOAL) to address it. SOAL avoids explicitly modeling changes or their dynamics, and instead shares information continuously. The key idea is that learners share a common small pool of experts, which they can use in a weighted adaptive way. We define group adaptive regret and prove that SOAL maintains known bounds on the adaptive regret obtained for single adaptive learners. Furthermore, it quickly adapts when learning tasks are related to each other. We demonstrate the benefits of the approach for two domains: vision and text. First, in the visual domain, we study a visual navigation task where a robot learns to navigate based on outdoor video scenes. We show how navigation can improve when knowledge from other robots in related scenes is available. Second, in the text domain, we create a new dataset for the task of assigning submitted papers to relevant editors. This is, inherently, an adaptive learning task due to the dynamic nature of research fields evolving in time. We show how learning to assign editors improves when knowledge from other editors is available. Together, these results demonstrate the benefits for sharing information across learners in concurrently changing environments.

Digital technologies as an adaptive learning tool in higher education

“…Zweig and Chechik (2017) argue that exchange of information between several teaching agents can accelerate adaptive learning [7]. This can be particularly useful if the learners work in a continually changing environment, because a learner can benefit from another learner's previous experience in order to adapt to some new environment.…”

Predicting Mining Industry Accidents with a Multitask Learning Approach

“…where tasks are grouped or exist in a hierarchy, or be related according to some general metric, 2. exploiting a priori unrelated tasks (Paredes et al 2012), where joint learning of unrelated tasks which use the same input data is deemed beneficial as it can lead to sparser and more informative representations for each task grouping, essentially by screening out biases present in the data, 3. knowledge transfer (Yosinski et al 2014), where a pretrained model can be used as a feature extractor to perform pre-processing for another learning algorithm, and 4. group online adaptive learning (GOAL) (Zweig and Chechik 2017) where sharing information is deemed particularly useful when models operate in continuously changing environments because a model could benefit from previous experience of another and quickly adapt to a new environment.…”