The Thirty-Seventh Annual Conference on Neural Information Processing Systems (NeurIPS 2023) 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)
Deep learning (e.g., architectures, generative models, optimization for deep networks)
Evaluation (e.g., methodology, meta studies, replicability and validity)
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.