What To Expect

The ML for Systems workshop presents cutting-edge work on ML in computer systems and aims to develop a unified methodology for the field.

Machine Learning (ML) for Systems describes the application of machine learning techniques to problems related to computer systems. By leveraging supervised learning and reinforcement learning (RL) approaches, machine learning can replace longstanding heuristics that currently drive many of these systems. This includes a wide range of topics, including multi-objective tasks such as designing new data structures ¹, integrated circuits ^{2, 3}, or design verification ^{20, 21}, as well as implementing control algorithms for applications such as compilers ^{12, 13, 19}, databases ⁸, memory management ^{9, 10}, or ML frameworks ¹¹. While the systems community increasingly recognizes the importance of ML in solving a variety of different systems problems ²³, ML for Systems remains an emerging area without widely established best practices, methods and strategies for the application of state-of-the-art machine learning techniques ²². The goal of this workshop is to provide an interdisciplinary venue for ML and Systems experts to push this boundary and start new directions within the ML for Systems area.

Workshop Direction

In previous 9 editions, we showcased specific approaches and frameworks to solve problems, bringing together researchers and practitioners at NeurIPS from both the ML and systems communities. While breaking new grounds, we encouraged collaborations and development in a broad range of ML for Systems works, many later published in top-tier conferences ^{11, 13, 14, 15, 16, 17, 18}. This year, we plan to continue this path while exploring how emerging ML paradigms—including large language models (LLMs), multimodal foundation models, and agentic workflows—can be leveraged to address systems challenges and improve the efficiency, reliability, and scalability of ML infrastructure itself.

Recently, the rise of LLMs, multimodal foundation models, and agentic workflows has presented new opportunities and challenges within the domain of computer systems. Our community is well-positioned to produce science and stimulate discussion for adapting to this new paradigm. We seek to explore both how these models can be used to solve systems problems, and how to address systems issues that emerge from large-scale training and serving of such models. Additionally, we place emphasis on developing best practices, methodologies, benchmarks, datasets, simulators, and evaluation frameworks that improve rigor, reproducibility, reliability, and trustworthiness in ML for Systems research.

Workshop Goals

NeurIPS provides a unique opportunity to bring together systems researchers and researchers from other sub-areas of ML who had not previously considered applying their techniques in a computer systems context. We see the goal of our workshop as solving the following two objectives:

• Opening up connections between research areas that were not previously considered, connecting the ML and Systems communities, growing the scope of ML for Systems work and unlocking new research opportunities.
• Developing best practices, methodologies and benchmarks for the ML for Systems field.

Our program will include a variety of speakers and poster sessions from selected papers. We invite researchers to submit relevant papers through our call for papers.