"Transfer Learning for Scientific Discovery" by Dr. Alexandru Niculescu-Mizil, IBM T.J. Watson Research center
Event details
| Date | 19.03.2010 |
| Hour | 14:00 |
| Speaker | Alexandru Niculescu-Mizil, IBM T.J. Watson Research center |
| Location | |
| Category | Conferences - Seminars |
Abstract
While receiving significant attention, learning Bayesian Network structure from data remains challenging, especially when training data is scarce. In this talk I show how structure learning performance can be significantly improved through inductive transfer, when data is available for multiple related problems. Departing from the traditional approach of learning the structures for each problem in isolation, I present a score and search algorithm for jointly learning multiple Bayesian Networks that improves the leaned structures by transferring useful information among the related problems.
Biography
Alexandru Niculescu-Mizil is a Herman Goldstine postdoctoral fellow at IBM T.J. Watson Research Center. He received his Ph.D. from Cornell University in 2008 under the supervision of Rich Caruana, a Masters of Science degree in Computer Science from Cornell University and a Magna Cum Laude Bachelors degree in Mathematics and Computer Science from University of Bucharest. His research interests are in machine learning and data mining, particularly in inductive transfer, graphical model structure learning, probability estimation, empirical evaluations, ensemble methods and on-line learning. He received an ICML Distinguished Student Paper Award in 2005 for his work on probability estimation, and a COLT Best Student in 2008 paper award for his work on on-line learning. In 2009 he led the IBM Research team that won the KDD Cup.
Links
Practical information
- General public
- Free
Contact
- Christine Moscioni