In this paper, we propose a novel semi-supervised learning (SSL) framework named
BoostMIS that combines adaptive pseudo labeling and informative active annotation to …

Data management is indispensable for informed decision-making in the big data era. In the
meantime, Large Language Models (LLMs), equipped with billions of model parameters and …

Federated learning (FL) enables multiple data owners to collaboratively train machine
learning (ML) models without disclosing their raw data. In the vertical federated learning …

In the wake of rapid advancements in artificial intelligence (AI), we stand on the brink of a
transformative leap in data systems. The imminent fusion of AI and DB (AI× DB) promises a …

Vertical federated learning (VFL) is an emerging paradigm for cross-silo organizations to
build more accurate machine learning (ML) models. In this setting, multiple organizations (ie …

In line with the increasing machine learning model inference accuracy, deep learning (DL)
models have been increasingly applied to structured data for a wide spectrum of real-world …

Multi-modal learning using unpaired labeled data from multiple modalities to boost the
performance of deep learning models on each individual modality has attracted a lot of …

Machine learning (ML) is an important part of modern data science applications. Data
scientists today have to manage the end-to-end ML life cycle that includes both model …