The coherent Ising machine (CIM) is designed to solve the NP-hard Ising problem quickly
and energy efficiently. Boolean satisfiability (SAT) and maximum satisfiability (Max-SAT) are …

The importance of explanations (XP's) of machine learning (ML) model predictions and of
adversarial examples (AE's) cannot be overstated, with both arguably being essential for the …

Accepted: 3 January 2022 Some digital data circulated through various social media,
regardless of its nature, requires high-level protection and security for various reasons. In …

While large pre-trained language models are powerful, their predictions often lack logical
consistency across test inputs. For example, a state-of-the-art Macaw question-answering …

Abstract Explanations of Machine Learning (ML) models often address a question. Such
explanations can be related with selecting feature-value pairs which are sufficient for the …

Tree ensembles (TEs) denote a prevalent machine learning model that do not offer
guarantees of interpretability, that represent a challenge from the perspective of explainable …

We propose and implement a family of quantum-informed recursive optimization (QIRO)
algorithms for combinatorial optimization problems. Our approach leverages quantum …

Decision lists (DLs) find a wide range of uses for classification problems in Machine
Learning (ML), being implemented in anumber of ML frameworks. DLs are often perceived …

Symmetry and dominance breaking can be crucial for solving hard combinatorial search and
optimisation problems, but the correctness of these techniques sometimes relies on subtle …

Large language models (LLMs) are expected to respond accurately but often exhibit
deficient reasoning or generate hallucinatory content. To address these, studies prefixed …