Feature selection helps reduce data acquisition costs in ML, but the standard approach is to
train models with static feature subsets. Here, we consider the dynamic feature selection …

Accurate artificial intelligence (AI) for disease diagnosis could lower healthcare workloads.
However, when time or financial resources for gathering input data are limited, as in …

Representation learning constitutes a pivotal cornerstone in contemporary deep learning
paradigms, offering a conduit to elucidate distinctive features within the latent space and …

This work focuses on a specific classification problem, where the information about a sample
is not readily available, but has to be acquired for a cost, and there is a per-sample budget …

Deep reinforcement learning (DRL) techniques have become increasingly used in various
fields for decision-making processes. However, a challenge that often arises is the trade-off …

Collecting relevant and high-quality data is critical to machine-learning-based application
development in automotive industry. It is highly desired to concentrate the connected vehicle …

The recent emergence of accurate artificial intelligence (AI) models for disease diagnosis
raises the possibility that AI-based clinical decision support could substantially lower the …

We introduce a challenging decision-making task that we call active acquisition for
multimodal temporal data (A2MT). In many real-world scenarios, input features are not …

The automation of the medical evidence acquisition and diagnosis process has recently
attracted increasing attention in order to reduce the workload of doctors and democratize …

A growing number of people are seeking healthcare advice online. Usually, they diagnose
their medical conditions based on the symptoms they are experiencing, which is also known …