In 2010, Kilian Weinberger joined the Computer Science & Engineering faculty at Washington University in St. Louis.
Previously, he was a research scientist at Yahoo! in Santa Clara, California, where he worked on next-generation spam filtering algorithms, multimedia search, high-dimensional data analysis and machine learning with convex optimization. His work on supervised and unsupervised metric learning has won several outstanding paper awards at CVPR, AISTATS and ICML.
Research for his 2012 National Science Foundation CAREER Award, "New Directions for Metric Learning," seeks to solve one of the fundamental problems of machine learning: how to compare individual texts, images or sounds.
Professor Weinberger is an area chair for NIPS and ICML, a member of the editorial board of JMLR, and senior program chair for AAAI - where he won the award for outstanding senior program committee member in 2011. He is publications chair for NIPS and ICML.
Professor Weinberger's research interests include multi-task learning, convex optimization, metric learning, dimensionality reduction, manifold learning and machine learned ranking.
Minmin Chen, Zhixiang (Eddie) Xu, Kilian Q. Weinberger, Fei Sha. Marginalized Stacked Denoising Autoencoders for Domain Adaptation. Proceedings of 29th International Conference on Machine Learning (ICML), 2012. (in press)
Minmin Chen, Kilian Q. Weinberger, and John C. Blitzer. Co-training for domain adaptation. In J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, and K. Weinberger, editors, Advances in Neural Information Processing Systems 24 (NIPS-24), pages 2456–2464. 2011.
Minmin Chen, Zhixiang (Eddie) Xu, Kilian Q. Weinberger, Olivier Chapelle, Dor Kedem. Classifier Cascade: Tradeoff between Accuracy and Feature Evaluation Cost. Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS), JMLR Workshop and Conference Proceedings 22: AISTATS 2012, pages 218-226, MIT Press.
Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal, Jennifer Paykin. Parallel Boosted Regression Trees for Web Search Ranking. Proceedings of the 20th international conference on World wide web, ACM, New York, USA, 2011. (in press)
Shibin Parameswaran and Kilian Q. Weinberger. Large Margin Multi-Task Metric Learning. In J. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R.S. Zemel, and A. Culotta (eds.), Advances in Neural Information Processing Systems 23 (NIPS), pages 1867-1875. 2010.