Vertical federated learning (VFL) is an emerging paradigm that allows different parties (eg,
organizations or enterprises) to collaboratively build machine learning models with privacy …

The relational data model was designed to facilitate large-scale data management and
analytics. We consider the problem of how to differentiate computations expressed …

The growing demand for advanced analytics beyond statistical aggregation calls for
database systems that support effective model selection of deep neural networks (DNNs) …

Relational database management systems (RDBMS) are widely used for the storage and
retrieval of structured data. To derive insights beyond statistical aggregation, we typically …

Modern machine learning (ML) systems commonly use stochastic gradient descent (SGD) to
train ML models. However, SGD relies on random data order to converge, which usually …

Deep learning (DL) training is data-intensive and often bottlenecked by fetching data from
remote storage. Recognizing that many samples' sizes diminish during data preprocessing …

We explore the training dynamics of neural networks in a structured non-IID setting where
documents are presented cyclically in a fixed, repeated sequence. Typically, networks suffer …

Extended SQL with machine learning (ML) predicates, commonly referred to as SQL+ ML,
integrates ML abilities into traditional SQL processing in databases. When processing SQL+ …

Databases are increasingly embracing AI to provide autonomous system optimization and
intelligent in-database analytics, aiming to relieve end-user burdens across various industry …

As companies store, process, and analyse bigger and bigger volumes of highly
heterogeneous data, novel research and technological challenges are emerging. Traditional …