New Perspectives on Machine Learning and Science


July 29, 2014


Rich Caruana and Isabelle Guyon


Microsoft Research, Chalearn


This session will look into the latest advances in areas of machine learning, such as causality, while also reviewing our understanding of topics such as deep learning.

This session will also highlight steps towards doing reproducible science by enabling researchers to share code and data, and experiments to help nurture an environment of scientific rigor. And it will open up new avenues for collaboration between researchers via the use of co-opetitions where people can cooperate with each other to reach a higher value than by merely competing.


Rich Caruana and Isabelle Guyon

Rich Caruana is a Senior Researcher at Microsoft Research. Before joining Microsoft, Rich was on the faculty at the Computer Science Department at Cornell University, at UCLA’s Medical School, and at CMU’s Center for Learning and Discovery (CALD). Rich’s Ph.D. is from Carnegie Mellon University, where he worked with Tom Mitchell and Herb Simon. His thesis on Multi-Task Learning helped generate interest in a new subfield of machine learning called Transfer Learning. Rich received an NSF CAREER Award in 2004 (for Meta Clustering), best paper awards in 2005 (with Alex Niculescu-Mizil), 2007 (with Daria Sorokina), and 2014 (with Todd Kulesza, Saleema Amershi, Danyel Fisher, and Denis Charles), co-chaired KDD in 2007 (with Xindong Wu), and serves as area chair for NIPS, ICML, and KDD. His current research focus is on learning for medical decision making, deep learning, adaptive clustering, and computational ecology.

Isabelle Guyon is an independent consultant, specialized in statistical data analysis, pattern recognition, and machine learning. Her areas of expertise include computer vision and bioinformatics. Her recent interest is in applications of machine learning to the discovery of causal relationships. Prior to starting her consulting practice in 1996, Isabelle Guyon was a researcher at AT&T Bell Laboratories, where she pioneered applications of neural networks to pen computer interfaces and co-invented Support Vector Machines (SVM), a machine learning technique, which has become a textbook method. She is also the primary inventor of SVM-RFE, a variable selection technique based on SVM. The SVM-RFE paper has thousands of citations and is often used as a reference method against which new feature selection methods are benchmarked. She also authored a seminal paper on feature selection that received thousands of citations. She organized many challenges in Machine Learning over the past few years supported by the EU network Pascal2, NSF, and DARPA, with prizes sponsored by Microsoft, Google, and Texas Instruments. Isabelle Guyon holds a Ph.D. degree in Physical Sciences from the University Pierre and Marie Curie, Paris, France. She is president of Chalearn, a non-profit dedicated to organizing challenges; vice-president of the Unipen foundation; adjunct professor at New-York University; action editor of the Journal of Machine Learning Research; and editor of the Challenges in Machine Learning book series of Microtome.