International Conference on Machine Learning (ICML 2009) in Montreal

Posted by Eyal Even Dar and Vahab Mirrokni, Google Research, NY

The 26th International Conference on Machine Learning (ICML 2009) was recently held in Montreal in conjunction with the 22nd Conference On Learning Theory (COLT 2009) and the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009). This is one of the major forums for researchers from both industry and academia to share the recent developments in the area of machine learning and artificial intelligence. Machine learning is a central area for Google as it has many applications in extracting useful information from a vast amount of data available on the web. In addition to sponsoring this scientific event, Google contributed intellectually to several scientific forums. Here's a short report of those activities:

• There were ten papers co-authored by Googlers in these conferences, which covered several areas of machine learning including domain adaption, online learning, bandits, boosting, sparsity and kernel learning.
• Corinna Cortes, the head of Google Research NY gave one of the three invited talks of ICML. She surveyed the last decade of research in learning kernels and highlighted both the successes and the failures in learning kernels with a focus on applications of convex optimization for this purpose. Corinna concluded with a call for applying new ideas and novel techniques to overcome the current obstacles.
• We presented a tutorial on Convergence of Natural Game Dynamics. This topic has received a lot of attention recently as it stands at the conflux of many fields such as economics, machine learning and theoretical computer science. In the tutorial, we surveyed the convergence properties of the most natural game dynamics such as the Nash dynamics or the best-response dynamics to the popular no-regret learning-based dynamics. The tutorial highlighted similarities and differences between the approaches in both the time of convergence, the point of convergence, and the quality of the outcome. We believe that the influence of the learning algorithms on the behavior of the users is an exciting and intriguing topic of research for many, and in particular for the analysis of ad auctions.
  

Google's main mission is "to organize the world's information and make it universally accessible and useful," and machine learning plays a fundamental role in both of these aspects. As a result, Google has invested significant resources in this area of research, and we look forward to continued participation and collaboration at these conferences for many more years.