In-context learning (ICL) is a type of prompting where a transformer model operates on a
sequence of (input, output) examples and performs inference on-the-fly. In this work, we …

Continual learning (CL), which aims to learn a sequence of tasks, has attracted significant
recent attention. However, most work has focused on the experimental performance of CL …

The goal of continual learning is to find a model that solves multiple learning tasks which are
presented sequentially to the learner. A key challenge in this setting is that the learner may" …

Constructing useful representations across a large number of tasks is a key requirement for
sample-efficient intelligent systems. A traditional idea in multitask learning (MTL) is building …

We provide a statistical analysis of regularization-based continual learning on a sequence of
linear regression tasks, with emphasis on how different regularization terms affect the model …

There are several opportunities for automation in healthcare that can improve clinician
throughput. One such example is assistive tools to document diagnosis codes when …

Continual learning (CL) is the research field that aims to build machine learning models that
can accumulate knowledge continuously over different tasks without retraining from scratch …

In the context of mobile sensing environments, various sensors on mobile devices
continually generate a vast amount of data. Analyzing this ever-increasing data presents …