This paper surveys the recent attempts, both from the machine learning and operations
research communities, at leveraging machine learning to solve combinatorial optimization …

The fast-paced development of computational tools has enabled tremendous scientific
progress in recent years. However, this rapid surge of technological capability also comes at …

Deep reinforcement learning (RL) algorithms are predominantly evaluated by comparing
their relative performance on a large suite of tasks. Most published results on deep RL …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

There has been a recent explosion of research on Large Language Models (LLMs) for
software engineering tasks, in particular code generation. However, results from LLMs can …

There has been a recent explosion of research on Large Language Models (LLMs) for
software engineering tasks, in particular code generation. However, results from LLMs can …

Deep learning (DL) training algorithms utilize nondeterminism to improve models' accuracy
and training efficiency. Hence, multiple identical training runs (eg, identical training data …

Artificial Intelligence (AI) can enable the development of next-generation autonomous safety-
critical systems in which Machine Learning (ML) algorithms learn optimized and safe …

Arguments that machine learning (ML) is facing a reproducibility and replication crisis
suggest that some published claims in research cannot be taken at face value. Concerns …

Science is experiencing an ongoing reproducibility crisis. In light of this crisis, our objective
is to investigate whether machine learning platforms provide out-of-the-box reproducibility …