The ALT 2018 conference is dedicated to all theoretical and algorithmic aspects of machine learning. We invite submissions with contributions to new or existing learning problems including, but not limited to:

• Design and analysis of learning algorithms.
• Statistical and computational learning theory.
• Online learning algorithms and theory.
• Optimization methods for learning.
• Unsupervised, semi-supervised, online and active learning.
• Connections of learning with other mathematical fields.
• Artificial neural networks, including deep learning.
• High-dimensional and non-parametric statistics.
• Learning with algebraic or combinatorial structure.
• Bayesian methods in learning.
• Planning and control, including reinforcement learning.
• Learning with system constraints: e.g. privacy, memory or communication budget.
• Learning from complex data: e.g., networks, time series, etc.
• Interactions with statistical physics.
• Learning in other settings: e.g. social, economic, and game-theoretic.

We are also interested in papers that include viewpoints that are new to the ALT community. We welcome experimental and algorithmic papers provided they are relevant to the focus of the conference by elucidating theoretical results, or by pointing out an interesting and not well understood behavior that could stimulate theoretical analysis.