Supervised learning is an important branch of machine learning (ML), which requires a
complete annotation (labeling) of the involved training data. This assumption is relaxed in …

Fine-tuning large models is highly effective, however, inference can be expensive and
produces carbon emissions. Knowledge distillation has been shown to be a practical …

News events are often associated with quantities (eg, the number of COVID-19 patients or
the number of arrests in a protest), and it is often important to extract their type, time, and …

This tutorial targets researchers and practitioners who are interested in ML technologies for
NLP from indirect supervision. In particular, we will present a diverse thread of indirect …

Real-world applications often require improved models by leveraging a range of cheap
incidental supervision signals. These could include partial labels, noisy labels, knowledge …

In this work we propose a pragmatic method that reduces the annotation cost for structured
label spaces using active learning. Our approach leverages partial annotation, which …

In a structured prediction problem, one needs to learn a predictor that, given a structured
input, produces a structured object, such as a sequence, tree, or clustering output …

Learning from indirect supervision signals is important in real-world AI applications when,
often, gold labels are missing or too costly. In this paper, we develop a unified theoretical …

In this position paper, we study interactive learning for structured output spaces, with a focus
on active learning, in which labels are unknown and must be acquired, and on skeptical …

In this article, we study the setting of learning from fuzzy labels, a generalization of
supervised learning in which instances are assumed to be labeled with a fuzzy set …