The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2025) is an interdisciplinary conference that brings together researchers in machine learning, neuroscience, statistics, optimization, computer vision, natural language processing, life sciences, natural sciences, social sciences, and other adjacent fields. We invite submissions presenting new and original research on topics including but not limited to the following: Applications (e.g., vision, language, speech and audio, Creative AI) Deep learning (e.g., architectures, generative models, optimization for deep networks, foundation models, LLMs) Evaluation (e.g., methodology, meta studies, replicability and validity, human-in-the-loop) General machine learning (supervised, unsupervised, online, active, etc.) Infrastructure (e.g., libraries, improved implementation and scalability, distributed solutions) Machine learning for sciences (e.g. climate, health, life sciences, physics, social sciences) Neuroscience and cognitive science (e.g., neural coding, brain-computer interfaces) Optimization (e.g., convex and non-convex, stochastic, robust) Probabilistic methods (e.g., variational inference, causal inference, Gaussian processes) Reinforcement learning (e.g., decision and control, planning, hierarchical RL, robotics) Social and economic aspects of machine learning (e.g., fairness, interpretability, human-AI interaction, privacy, safety, strategic behavior) Theory (e.g., control theory, learning theory, algorithmic game theory) Machine learning is a rapidly evolving field, and so we welcome interdisciplinary submissions that do not fit neatly into existing categories. We also encourage in-depth analysis of existing methods that provide new insights in terms of their limitations or behaviour beyond the scope of the original work.