Publications

  • Model Repair: Robust recovery of over-paramaterized statistical models
    Chao Gao and John Lafferty
    arXiv:2005.09912

  • TopicEq: A joint topic and mathematical equation model for scientific texts
    Michihiro Yasunaga and John Lafferty
    AAAI, 2019.

  • Testing for global network structure using small subgraph statistics
    Chao Gao and John Lafferty
    arXiv:1710.00862

  • Testing network structure using relations between small subgraph probabilities
    Chao Gao and John Lafferty
    arXiv:1704.06742

  • Convergence analysis for rectangular matrix completion using Burer-Monteiro factorization and gradient descent
    Qinqing Zheng and John Lafferty
    arxiv:1605.07051

  • Quantized estimation of Gaussian sequence models in Euclidean balls
    Yuancheng Zhu and John Lafferty
    Advances in Neural Information Processing Systems 28, 2014.
    [pdf]

  • Blossom tree graphical models
    Zhe Liu and John Lafferty
    Advances in Neural Information Processing Systems 28, 2014
    [pdf]

  • Computation-risk tradeoffs for covariance-thresholded regression
    Dinah Shender and John Lafferty
    International Conference on Machine Learning, JMLR W&CP Vol. 28 (3) : 756–764, 2013.
    [pdf]

  • The bigraphical lasso
    Alfredo Kalaitzis, John Lafferty, Neil Lawrence, and Shuheng Zhou
    International Conference on Machine Learning,
    JMLR W&CP 28 (3) : 1229–1237    , 2013.
    [pdf]

  • Nonparametric reduced rank regression
    Rina Foygel, Michael Horrell, Mathias Drton, and John Lafferty
    Advances in Neural Information Processing Systems 25, pp 1637–1645, 2012.
    [pdf arxiv:1301.1919]

  • Exponential concentration for mutual information estimation with application to forests
    Han Liu, John Lafferty, Larry Wasserman
    Advances in Neural Information Processing Systems 25, pp 2546–2554, 2012.
    [pdf]

  • High-dimensional semiparametric Gaussian copula graphical models
    Han Liu, Fang Han, Ming Yuan, John D. Lafferty and Larry A. Wasserman
    Ann. Statist. Volume 40, Number 4, 2012, 2293–2326.
    [link]

  • Sparse nonparametric graphical models
    John Lafferty, Han Liu, and Larry Wasserman
    Statist. Sci. Volume 27, Number 4, 2012, 519–537.
    [link]

  • Sequential nonparametric regression
    Haijie Gu and John D. Lafferty,
    Proceedings of the 29th International Conference on Machine
    Learning, ICML 2012, Edinburgh, Scotland.
    [pdf]

  • Sparse additive functional and kernel CCA
    Sivaraman Balakrishnan, Kriti Puniyani and John D. Lafferty
    Proceedings of the 29th International Conference on Machine
    Learning, ICML 2012, Edinburgh, Scotland.
    [pdf]

  • Conditional sparse coding and grouped multivariate regression
    Min Xu and John D. Lafferty
    Proceedings of the 29th International Conference on Machine
    Learning, ICML 2012, Edinburgh, Scotland.
    [pdf]

  • The nonparanormal SKEPTIC
    Han Liu, Fang Han, Ming Yuan, John D. Lafferty and Larry A. Wasserman
    Proceedings of the 29th International Conference on Machine
    Learning, ICML 2012, Edinburgh, Scotland.
    [pdf]

  • Graph-valued regression
    Han Liu, Xi Chen, John Lafferty and Larry Wasserman
    Advances in Neural Information Processing Systems 23,
    pp 1423-1431, 2010
    [pdf]
    [arxiv]

  • 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
    [link]

  • Union support recovery in multi-task learning
    Mladen Kolar, John Lafferty and Larry Wasserman
    J. Machine Learning Research, Vol 12, pp 2415–2435, 2011.
    [jmlr]

  • Forest density estimation

    Han Lu, Min Xu, Haijie Gu, Anupam Gupta, John Lafferty and Larry Wasserman
    J. Machine Learning Research, Vol. 12, pp 907–951, 2011.
    [link]

  • Time varying undirected graphs
    Shuheng Zhou, John Lafferty and Larry Wasserman
    Machine Learning,
    Volume 80, Numbers 2-3, September 2010, pp. 295-319.
    [link]

  • Topic models
    David Blei and John Lafferty
    Text Mining: Classification, Clustering, and Applications
    Srivastava, A. and Sahami, M., Eds), Taylor & Francis, London, England, 2009.

    [pdf]

  • The nonparanormal: Semiparametric estimation of high dimensional undirected graphs
    Han Liu, John Lafferty and Larry Wasserman
    Journal of Machine Learning Research,
    Volume 10, pp 2295-2328, 2009.
    [pdf]

  • 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.
    [link]
    [pdf]

  • Large-scale collaborative prediction using a nonparametric random effects model
    Kai Yu, John Lafferty, Shenghuo Zhu and Yihong Gong
    Machine Learning: Proceedings of the Twenty-Sixth International
    Conference     (ICML), 2009
    [pdf]

  • Fast nonparametric matrix factorization for large-scale collaborative filtering
    Kai Yu, Shenghuo Zhu, John Lafferty and Yihong Gong
    2009 ACM SIGIR Conference on Research and Development in
    Information Retrieval     (SIGIR), 2009
    [pdf]

  • Visualizing topics with multiword expressions
    David Blei and John Lafferty
    [arXiv:0907.1013]

  • Nonparametric regression and classification with joint sparsity constraints
    Han Liu, John Lafferty and Larry Wasserman
    In Advances in Neural Information Processing Systems (NIPS),
    21, 2008
    [pdf]

  • Time varying undirected graphs
    Shuheng Zhou, John Lafferty and Larry Wasserman
    Conference on Learning Theory (COLT), 2008
    [pdf]

  • Compressed and privacy sensitive sparse regression
    Shuheng Zhou, John Lafferty and Larry Wasserman
    IEEE Transactions on Information Theory, Volume,55, Issue 2, pp 846-866, 2009
    [link],
    [arxiv]

  • Rodeo: sparse, greedy nonparametric regression
    John Lafferty and Larry Wasserman
    The Annals of Statistics, Vol. 36, No.1, 2008, pages 28-63
    [pdf]

  • Comments on: Nonparametric inference with generalized likelihood tests: Nonparametric sparsity

    John Lafferty and Larry Wasserman
    Test, Vol. 16, pp. 453–455, 2007.

  • SpAM: Sparse additive models
    Pradeep Ravikumar, Han Liu, John Lafferty and Larry Wasserman
    In Advances in Neural Information Processing Systems (NIPS),
    20, 2007
    [pdf]

  • Statistical analysis of semisupervised regression
    John Lafferty and Larry Wasserman
    In Advances in Neural Information Processing Systems (NIPS),
    20, 2007
    [pdf]

  • Compressed regression
    Shuheng Zhou, John Lafferty and Larry Wasserman
    In Advances in Neural Information Processing Systems (NIPS),
    20, 2007
    [pdf]

  • Computationally efficient M-estimation of log-linear structure models
    Noah Smith, John Laferty and Doug Vail
    in Proceedings of Conference of the Association for Computational Linguistics, 2007
    [pdf]

  • A correlated topic model of Science
    David Blei and John Lafferty
    Annals of Applied Statistics, Vol. 1, No. 1, 17-35, 2007
    [pdf]