The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …

Data-driven paradigms are well-known and salient demands of future wireless
communication. Empowered by big data and machine learning techniques, next-generation …

Distributed training of foundation models, especially large language models (LLMs), is
communication-intensive and so has heavily relied on centralized data centers with fast …

Kernel methods provide an elegant and principled approach to nonparametric learning, but
so far could hardly be used in large scale problems, since naïve implementations scale …

In order to scale standard Gaussian process (GP) regression to large-scale datasets,
aggregation models employ factorized training process and then combine predictions from …

Scalable and efficient distributed learning is one of the main driving forces behind the recent
rapid advancement of machine learning and artificial intelligence. One prominent feature of …

Recent years have witnessed a growing list of systems for distributed data-parallel training.
Existing systems largely fit into two paradigms, ie, parameter server and MPI-style collective …

Gaussian process regression (GPR) is an important nonparametric learning method in
machine learning research with many real-world applications. It is well known that training …

Gaussian Processes (GPs) are powerful non-parametric Bayesian models for function
estimation, but suffer from high complexity in terms of both computation and storage. To …

Deep learning based models have dominated the current landscape of production
recommender systems. Furthermore, recent years have witnessed an exponential growth of …