I'm a faculty member in the School of
Computer Science at Carnegie
Mellon University. My research is in machine learning and statistics, with
basic research on theory, methods, and algorithms. Areas of focus
include nonparametric methods, sparsity, the analysis of
high dimensional data, graphical models, information theory, and
applications in language processing, computer vision, and information
retrieval.
Some research projects
Selected publications more
- Graph-valued regression
Han Liu, Xi Chen, John Lafferty and Larry Wasserman
Advances in Neural Information Processing Systems 23, pp 1423-1431, 2010. - High dimensional Ising
model selection using ℓ1-regularized logistic regression
Pradeep Ravikumar, Martin Wainwright and John Lafferty
Ann. Statist., Vol. 38, No. 3, pp 1287-1319, 2010. - Compressed and privacy-sensitive sparse regression
Shuheng Zhou, John Lafferty and Larry Wasserman
IEEE Trans. Info. Theory, Vol. 55, Issue 2, Feb. 2009, pp 846--866.
arxiv:0706.0534v2 - The nonparanormal: Semiparametric estimation
of high dimensional undirected graphs
Han Liu, John Lafferty and Larry Wasserman
Journal of Machine Learning Research, Vol. 10, pp 2295-2328, 2009. - Sparse additive models
Pradeep Ravikumar, John Lafferty, Han Liu and Larry Wasserman
Journal of the Royal Statistical Society, Series B, (Statistical Methodology) Vol. 71, Issue 5, pp 1009-1030, November 2009. - Rodeo: Sparse, greedy, nonparametric regression
John Lafferty and Larry Wasserman
Ann. Statist., Vol. 36, No. 1 (2008), pp 28-63.
- A correlated topic model of Science
David Blei and John Lafferty
Ann. Appl. Stat., Vol. 1, No. 1, 17-35, 2007. - Preconditioner approximations for
probabilistic graphical models
Pradeep Ravikumar and John Lafferty
Advances in Neural Information Processing Systems 18, pp 1113-1120, 2006. - A risk minimization framework for information
retrieval
Chengxiang Zhai and John Lafferty
Information Processing and Management 42(1), pp 31-55, 2006. - Diffusion kernels on statistical
manifolds
John Lafferty and Guy Lebanon
Journal of Machine Learning Research, Vol. 6, pp 129-163, 2005. - Semi-supervised learning using Gaussian
fields and harmonic functions
Xiaojin Zhu, Zoubin Ghahramani and John Lafferty
Proceedings of the Twentieth International Conference on Machine Learning (ICML), 2003.