Call for papers

We invite original research papers on machine learning. Topics include, but are not limited to:

• General machine learning (supervised, unsupervised, online, active learning)
• Deep learning (architectures, generative models)
• Optimization
• Probabilistic methods
• Reinforcement learning
• Learning theory
• Fairness, privacy, robustness and interpretability
• Evaluation and datasets
• Machine learning systems and infrastructure
• Applications (vision, language, science, health)

• Papers must be original work. Do not submit a paper that is published, accepted or under review somewhere else.
• Reviewing is double-blind. Remove author names, acknowledgements and any links that identify you.
• You may post your paper as a public preprint during review, but do not advertise it as a Machine Learning & Snow 2027 submission.
• Papers presented at workshops without proceedings may be submitted.
• At least one author of each accepted paper must register and present it in person. Remote presentations are not possible.