Many machine learning (ML) algorithms are inherently random–multiple executions using
the same inputs may produce slightly different results each time. Randomness impacts how …

Many Machine Learning (ML) algorithms are in-herently random in nature-executing them
using the same inputs may lead to slightly different results across different runs. Such …

Testing Machine Learning (ML) projects is challenging due to inherent non-determinism of
various ML algorithms and the lack of reliable ways to compute reference results …

The development process for reinforcement learning applications is still exploratory rather
than systematic. This exploratory nature reduces reuse of specifications between …

We propose a novel tool, AquaSense, to automatically reason about the sensitivity analysis
of probabilistic programs. In the context of probabilistic programs, sensitivity analysis …

Probabilistic programming aims to open the power of Bayesian reasoning to software
developers and scientists, but identification of problems during inference and debugging are …

Probabilistic programming aims to open the power of Bayesian reasoning to software
developers and scientists, but identification of problems during inference and debugging are …

Blockchain (BC) technology provides a secure distributed transactional database, that can
enhance the security and privacy of decentralized systems and applications, eg distributed …

The series Lecture Notes in Computer Science (LNCS), including its subseries Lecture
Notes in Artificial Intelligence (LNAI) and Lecture Notes in Bioinformatics (LNBI), has …

Probabilistic programming simplifies the encoding of statistical models as straightforward
programs. At its core, it employs an inference algorithm which automate the model inference …