The 5th Workshop on Machine Learning and Systems (EuroMLSys)

Call for Papers

A growing area of interest in machine intelligence is at the intersection of AI/ML and systems design. At the same time, applications of ML are growing in complexity and so is the volume of data they produce/consume. For computer systems to scale, new learning approaches and advanced optimisation techniques are needed. We also need to understand better the current AI/ML frameworks, in terms of their functionality, limitations, and target applications. This will clarify potential desired functions and future architectures. Novel machine learning methods to optimise and accelerate software and hardware systems must also be developed.

EuroMLSys is an interdisciplinary workshop that brings together researchers in computer architecture, systems and machine learning, along with practitioners who are active in these emerging areas.

Topics of interest include, but are not limited to, the following:

• Scheduling algorithms for data processing clusters
• Custom hardware for machine learning
• Programming languages for machine learning
• Benchmarking systems (for machine learning algorithms)
• Synthetic input data generation for training
• Systems for training and serving machine learning models at scale
• Graph neural networks
• Neural network compression and pruning in systems
• Systems for incremental learning algorithms
• Large scale distributed learning algorithms in practice
• Database systems for large scale learning
• Model understanding tools (debugging, visualisation, etc.)
• Systems for model-free and model-based Reinforcement Learning
• Optimisation in end-to-end deep learning
• System optimisation using Bayesian Optimisation
• Acceleration of model building (e.g., imitation learning in RL)
• Use of probabilistic models in ML/AI application
• Learning models for inferring network attacks, device/service fingerprinting, congestion, etc.
• Techniques to collect and analyze network data in a privacy-preserving manner
• Learning models to capture network events and control actions
• Machine learning in networking (e.g., use of Deep RL in networking)
• Analysis of distributed ML algorithms
• Semantics for distributed ML languages
• Probabilistic modelling for distributed ML algorithms
• Synchronisation and state control of distributed ML algorithms
• ML Compiler Optimisation
• Optimisation in Large Language Model (LLM)
• Novel approaches to identify and mitigate bias in ML systems
• Enhancing transparency and interpretability for fair AI
• ML systems promoting equity, fairness, and diversity
• Examining the societal and ecological impacts of ML systems

Accepted papers will be published in the ACM Digital Library (you can opt out from this).