Despite the advancement of machine learning techniques in recent years, state-of-the-art
systems lack robustness to “real world” events, where the input distributions and tasks …

The volume of e-commerce continues to increase year after year. Buying goods on the
internet is easy and practical, and took a huge boost during the lockdowns of the Covid …

We consider a continual learning (CL) problem with two linear regression tasks in the fixed
design setting, where the feature vectors are assumed fixed and the labels are assumed to …

A major problem with Active Learning (AL) is high training costs since models are typically
retrained from scratch after every query round. We start by demonstrating that standard AL …

One notable weakness of current machine learning algorithms is the poor ability of models
to solve new problems without forgetting previously acquired knowledge. The Continual …

Implicit neural representations (INRs) have emerged as powerful tools for the continuous
representation of signals, finding applications in imaging, computer graphics, and signal …

Continual learning and multi-task learning are commonly used machine learning techniques
for learning from multiple tasks. However, existing literature assumes multi-task learning as …

Multi-hop question answering (QA) requires reasoning over multiple documents to answer a
complex question and provide interpretable supporting evidence. However, providing …

An important problem in machine learning is the ability to learn tasks in a sequential
manner. If trained with standard first-order methods most models forget previously learned …

While active learning (AL) improves the labeling efficiency of machine learning (by allowing
models to query the labels of data samples), a major problem is that compute efficiency is …