ALADDIN
CENTER Carnegie Mellon UniversityCarnegie Mellon Computer Science DepartmentSchool of Computer Science
Papers
 
Aladdin
About
News and Events
Calendar
People
PROBEs
Workshops
Papers
Education
Seminars
Courses
Related Activities
Corporate
Related Links
Intranet
Contact
 
Captcha
REUs
Outreach Roadshow

Search result for: Lafferty   all years

Avrim Blum, John Lafferty, Mugizi Robert Rwebangira, and Rajashekar Reddy: Semi-Supervised Learning Using Randomized Mincuts. Proceedings of the 21st International Conference on Machine Learning (ICML)   PDF
John Lafferty, Xiaojin Zhu, and Yan Liu: Kernel conditional random fields: Representation and clique selection. Proceedings of the 21st International Conference on Machine Learning (ICML), 2004   PDF
Xiaojin Zhu, Zoubin Ghahramani, and John Lafferty: Semi-supervised learning using Gaussian fields and harmonic functions. Twentieth International Conference on Machine Learning (ICML-2003)   PDF
Xiaojin Zhu, John Lafferty, and Zoubin Ghahramani: Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions. ICML 2003 Workshop on The Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining   PDF
Guy Lebanon, John Lafferty: Boosting and maximum likelihood for exponential models. Technical Report CMU-CS-01-144, School of Computer Science, CMU, 2001. Shorter version to appear in Advances in Neural Information Processing Systems (NIPS), 14, 2001   PDF
John Lafferty, Fernando Pereira, Andrew McCallum: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. International Conference on Machine Learning (ICML'01), 2001   PDF
John Lafferty, Larry Wasserman: Iterative Markov chain Monte Carlo computation of reference priors and minimax risk. Uncertainty in Artificial Intelligence (UAI'01), 2001   PDF
John Lafferty, Chengxiang Zhai: Probabilistic IR models based on document and query generation. Abstract in the Proceedings of the Workshop on Language Modeling and Information Retrieval, Carnegie Mellon University, 2001   PDF
John Lafferty, Chengxiang Zhai: Document Language Models, Query Models, and Risk Minimization for Information Retrieval. SIGIR 2001: 111-119   PDF
Chengxiang Zhai, John Lafferty: Model-based Feedback in the Language Modeling Approach to Information Retrieval. CIKM 2001: 403-410   PDF
Chengxiang Zhai, John Lafferty: A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval. SIGIR 2001: 334-342   PDF
Stephen Della Pietra, Vincent Della Pietra, John Lafferty: Duality and auxiliary functions for Bregman distances. Technical Report CMU-CS-01-109, School of Computer Science, CMU, 2001   PDF
John Lafferty, Dan Rockmore: Codes and iterative decoding on algebraic expander graphs. In International Symposium on Information Theory and its Applications, 2000.   PDF

 

This material is based upon work supported by National Science Foundation under Grant No. 0122581.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the
National Science Foundation