Abstract Machine learning (ML) provides algorithms to create computer programs based on
data without explicitly programming them. In business process management (BPM), ML …

Many promising approaches to symbolic regression have been presented in recent years,
yet progress in the field continues to suffer from a lack of uniform, robust, and transparent …

Abstract Machine learning (ML) is becoming increasingly crucial in many fields of
engineering but has not yet played out its full potential in bioprocess engineering. While …

Recent studies have shown that deep neural net-works (DNNs) are vulnerable to
adversarial attacks, including evasion and backdoor (poisoning) attacks. On the defense …

Symbolic regression (SR) algorithms attempt to learn analytic expressions which fit data
accurately and in a highly interpretable manner. Conventional SR suffers from two …

Symbolic regression (SR) aims to discover concise closed-form mathematical equations
from data, a task fundamental to scientific discovery. However, the problem is highly …

For the past six years, researchers in genetic programming and other program synthesis
disciplines have used the General Program Synthesis Benchmark Suite to benchmark many …

Data privacy is a major concern in industries such as healthcare or finance. The requirement
to safeguard privacy is essential to prevent data breaches and misuse, which can have …

Artificial intelligence (AI) and machine learning (ML) techniques have existed for years, but
new hardware trends and advances in model training and inference have radically improved …

Symbolic regression searches for analytic expressions that accurately describe studied
phenomena. The main promise of this approach is that it may return an interpretable model …