Submission topics
Frame a research question, technical talk or implementation case study across the existing 16 topic areas. Explain the baseline, data conditions, evaluation choices and deployment constraints behind a machine learning decision.
ML FORWARD SUMMIT
Call for Papers
Submit original machine learning research by December 1, 2026.
February 2-3, 2027 / Berlin, Germany
Submission preparation
Describe your methods, measured results and limitations. Explain what changed after deployment where relevant, including negative results or lessons from retired systems. Acknowledge collaborators and sources; omit confidential material or personal data without permission.
Research guidance
Consider temporal validation and leakage, calibration and uncertainty, feature freshness, offline policy evaluation, retraining triggers, model compression and maintenance costs. Submission does not imply acceptance or publication.
Abstract Submission & Presentation
Abstracts must be submitted by December 1, 2026 through the Abstract Submission page. Upload a PDF, DOC or DOCX document, up to 20 MB. Content must be original, unpublished, and free of plagiarism. Accepted authors must register before the deadline to secure their presentation slot.
The conference organizers reserve the right to reject abstracts without explanation. Plagiarism, copyright infringement, or other intellectual property violations will result in immediate rejection.
Formatting Guidelines:
- Single-spaced, 11-point font, Times New Roman or Arial.
- Maximum of 2 pages, including title page, abstract, and keywords.
- Abstract should be 150-250 words.
- Final presentation files must be submitted at least 7 days before the conference.
Research and session areas
Explore the scope of each area when preparing your contribution.
- LEARNING THEORY & OPTIMISATION
- DEEP LEARNING & REPRESENTATION LEARNING
- NLP & LARGE LANGUAGE MODELS
- COMPUTER VISION & PERCEPTION
- GENERATIVE & MULTIMODAL LEARNING
- REINFORCEMENT LEARNING & DECISION-MAKING
- GRAPH LEARNING & RECOMMENDER SYSTEMS
- TIME SERIES & FORECASTING
- CAUSAL & PROBABILISTIC MACHINE LEARNING
- DATA-CENTRIC ML & SYNTHETIC DATA
- MLOPS & PRODUCTION ML SYSTEMS
- EFFICIENT ML & EDGE AI
- FEDERATED & PRIVACY-PRESERVING LEARNING
- EVALUATION, SAFETY & RESPONSIBLE ML
- ML FOR SCIENCE, HEALTH & CLIMATE
- ROBOTICS & EMBODIED LEARNING