Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …

Automated machine learning (AutoML) can make data scientists more productive. But if
machine learning is totally automated, that leaves no room for data scientists to apply their …

We address the problem of hyper-parameter optimization (HPO) for federated learning (FL-
HPO). We introduce Federated Loss SuRface Aggregation (FLoRA), a general FL-HPO …

AutoML systems build machine learning models automatically by performing a search over
valid data transformations and learners, along with hyper-parameter optimization for each …

AutoML automatically selects, composes and parameterizes machine learning algorithms
into a workflow or pipeline of operations that aims at maximizing performance on a given …

Imaging systems consist of cameras to encode visual information about the world and
perception models to interpret this encoding. Cameras contain (1) illumination sources,(2) …

Automated machine learning makes it easier for data scientists to develop pipelines by
searching over possible choices for hyperparameters, algorithms, and even pipeline …

The process of extracting valuable and novel insights from raw data involves a series of
complex steps. In the realm of Automated Machine Learning (AutoML), a significant research …

Finding multiple high-quality plans is essential in many planning applications, and top-k
planning asks for finding the k best plans, naturally extending cost-optimal classical …

" Human-aware" has become a popular keyword used to describe a particular class of AI
systems that are designed to work and interact with humans. While there exists a surprising …