v22: AISTATS 2012 Proceedings

2026-06-10 · Source: Proceedings of Machine Learning Research · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

Volume 22 of the Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2012), held from April 21-23, 2012, in La Palma, Canary Islands, presents a diverse collection of research at the intersection of AI and statistics. Edited by Neil D. Lawrence and Mark Girolami, the volume features papers on advanced machine learning techniques, including various forms of bandit problems, sparse modeling, and deep learning architectures. Key contributions address topics such as causal inference, graphical models, clustering, and optimization methods, alongside applications in areas like risk management, physiological monitoring, and speech analysis. The proceedings highlight significant advancements in statistical inference, learning theory, and computational efficiency for complex AI systems. This collection offers valuable insights into the state-of-the-art research in AI and statistics from that period.

Key takeaway

Volume 22 compiles over 100 peer-reviewed papers from the 2012 International Conference on Artificial Intelligence and Statistics (AISTATS). It features foundational research across sparse stochastic bandits, graphical models, causal inference, deep learning, and various machine learning algorithms. This collection provides critical historical context and methodological advancements for researchers and practitioners in AI, statistics, and related applied sciences.

Topics

• Sparse Learning
• Bandit Algorithms
• Graphical Models
• Bayesian Inference
• Clustering Algorithms

Best for: AI Scientist, Research Scientist, Data Scientist

Related on AIssential

• v51: AISTATS 2016 Proceedings — Volume 51 of the Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), held in Cadiz, Spain, from May 9-11, 2016, features a comprehensive collection of research papers.
• v31: AISTATS 2013 Proceedings — Volume 31 compiles the proceedings from the Sixteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2013), held from May 29-June 1, 2013, in Scottsdale, Arizona, USA, and edited by Carlos M.
• The Sample Complexity of Multiclass and Sparse Contextual Bandits — Algorithms achieve optimal sample complexity for s-sparse contextual bandits, closing a significant gap in prior work.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Proceedings of Machine Learning Research.