Abstract Machine Learning (ML) models are ubiquitous in real-world applications and are a
constant focus of research. Modern ML models have become more complex, deeper, and …

As data privacy increasingly becomes a critical societal concern, federated learning has
been a hot research topic in enabling the collaborative training of machine learning models …

This article provides a birds-eye view on the role of decision trees in machine learning and
data science over roughly four decades. It sketches the evolution of decision tree research …

Federated learning is the process of develo** machine learning models over datasets
distributed across data centers such as hospitals, clinical research labs, and mobile devices …

Abstract Gradient Boosting Decision Trees (GBDTs) have become very successful in recent
years, with many awards in machine learning and data mining competitions. There have …

Bayesian optimization (BO) is a prominent approach to optimizing expensive-to-evaluate
black-box functions. The massive computational capability of edge devices such as mobile …

With the ever-evolving concerns on privacy protection, vertical federated learning (FL),
where participants own non-overlap** features for the same set of instances, is becoming …

While the quality of machine learning services largely relies on the volume of training data,
data regulations such as the General Data Protection Regulation (GDPR) impose stringent …

Federated Learning (FL) has been an emerging trend in machine learning and artificial
intelligence. It allows multiple participants to collaboratively train a better global model and …

Bayesian optimization (BO) has recently been extended to the federated learning (FL)
setting by the federated Thompson sampling (FTS) algorithm, which has promising …