Abstract Machine learning has become the state-of-the-art technique for many tasks
including computer vision, natural language processing, speech processing tasks, etc …

Recently, research on short text topic models has addressed the challenges of social media
datasets. These models are typically evaluated using automated measures. However, recent …

Advances in artificial intelligence, sensors and big data management have far-reaching
societal impacts. As these systems augment our everyday lives, it becomes increasing-ly …

We have recently seen many successful applications of recurrent neural networks (RNNs)
on electronic medical records (EMRs), which contain histories of patients' diagnoses …

Machine learning is an important tool for decision making, but its ethical and responsible
application requires rigorous vetting of its interpretability and utility: an understudied …

How can we design Natural Language Processing (NLP) systems that learn from human
feedback? There is a growing research body of Human-in-the-loop (HITL) NLP frameworks …

In this paper, we make the case that interpretability and explainability are distinct
requirements for machine learning systems. To make this case, we provide an overview of …

Topic modeling includes a variety of machine learning techniques for identifying latent
themes in a corpus of documents. Generating an exact solution (ie, finding global optimum) …

The use of Large Language Models (LLMs) for writing has sparked controversy both among
readers and writers. On one hand, writers are concerned that LLMs will deprive them of …

Human-in-the-loop topic modeling allows users to guide the creation of topic models and to
improve model quality without having to be experts in topic modeling algorithms. Prior work …