In recent years, machine learning has transitioned from a field of academic research interest
to a field capable of solving real-world business problems. However, the deployment of …

As a powerful expression of human knowledge in a structural form, knowledge graph (KG)
has drawn great attention from both the academia and the industry and a large number of …

Language models (LMs) are becoming the foundation for almost all major language
technologies, but their capabilities, limitations, and risks are not well understood. We present …

Foundation Models (FMs) are models trained on large corpora of data that, at very large
scale, can generalize to new tasks without any task-specific finetuning. As these models …

With the increasing importance and complexity of data pipelines, data quality became one of
the key challenges in modern software applications. The importance of data quality has …

This is an overview of the end-to-end data cleaning process. Data quality is one of the most
important problems in data management, since dirty data often leads to inaccurate data …

We introduce HoloClean, a framework for holistic data repairing driven by probabilistic
inference. HoloClean unifies existing qualitative data repairing approaches, which rely on …

Deep learning based techniques have been recently used with promising results for data
integration problems. Some methods directly use pre-trained embeddings that were trained …

We introduce a few-shot learning framework for error detection. We show that data
augmentation (a form of weak supervision) is key to training high-quality, ML-based error …

We discuss scalable data integration challenges in the enterprise inspired by our
experience at Tamr1. We use multiple real customer examples to highlight the technical …