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Model Repair: Robust recovery of over-paramaterized statistical models
Chao Gao and John Lafferty
arXiv:2005.09912
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Surfing: Iterative optimization over incrementally trained deep networks
Ganlin Song, Fan Zhou, and John Lafferty
Advances in Neural Information Processing Systems 32,2019
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Prediction rule reshaping
Matt Bonakdarpour, Sabyasachi Chatterjee, Rina Barber, and John Lafferty
International Conference on Machine Learning (ICML), 2018.
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Distributed nonparametric regression under communication constraints
Yuancheng Zhu and John Lafferty
International Conference on Machine Learning (ICML), 2018.
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TopicEq: A joint topic and mathematical equation model for scientific texts
Michihiro Yasunaga and John Lafferty
AAAI, 2019.
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Denoising flows on trees
Sabyasachi Chatterjee and John Lafferty
IEEE Trans. Info. Theory, Vol. 64, No. 3, March 2018
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ESP: A Machine Learning Approach to Predicting Application Interference
Nikita Mishra, John Lafferty, and Henry Hoffmann
Proceedings of International Conference on Autonomic Computing, 2017.
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Testing for global network structure using small subgraph statistics
Chao Gao and John Lafferty
arXiv:1710.00862
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Testing network structure using relations between small subgraph probabilities
Chao Gao and John Lafferty
arXiv:1704.06742
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Selective inference for group-sparse linear models
Advances in Neural Information Processing Systems 29, 2016.
Fan Yang, Rina Foygel Barber, Prateek Jain, and John Lafferty
arxiv:1607.08211
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Local minimax complexity of stochastic convex optimization
Yuancheng Zhu, Sabyasachi Chatterjee, John Duchi and John Lafferty
Advances in Neural Information Processing Systems 29, 2016.
arxiv:1605.07596
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Convergence analysis for rectangular matrix completion using Burer-Monteiro factorization and gradient descent
Qinqing Zheng and John Lafferty
arxiv:1605.07051
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Adaptive risk bounds in unimodal regression
Sabyasachi Chatterjee and John Lafferty
Bernoulli, Volume 25, Number 1 (2019), 1-25
arxiv:1512.02956
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A convergent gradient descent algorithm for rank minimization and semidefinite programming from random linear measurements
Qinqing Zheng and John Lafferty
Advances in Neural Information Processing Systems 28, 2015.
arxiv:1506.06081
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Quantized nonparametric estimation over Sobolev ellipsoids
Yuancheng Zhu and John Lafferty
Information and Inference, Volume 7, Issue 1, March 2018, Pages 31–82
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Faithful variable selection for high dimensional convex regression
Min Xu, Minhua Chen and John Lafferty
Ann. Stat., Vol. 44, No. 6, 2624-2660, 2016.
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A graphical model approach to minimizing energy under performance constraints
Nikita Mishra, Harper Zhang, John Lafferty and Henry Hoffman
International Conference on Architectural Support for Programming Languages and Operating Systems
(ASPLOS), 2015. [poster]
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Quantized estimation of Gaussian sequence models in Euclidean balls
Yuancheng Zhu and John Lafferty
Advances in Neural Information Processing Systems 28, 2014.
[pdf]
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Blossom tree graphical models
Zhe Liu and John Lafferty
Advances in Neural Information Processing Systems 28, 2014
[pdf]
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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]
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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]
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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]
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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]
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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]
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Sparse nonparametric graphical models
John Lafferty, Han Liu, and Larry Wasserman
Statist. Sci. Volume 27, Number 4, 2012, 519–537.
[link]
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Sequential nonparametric regression
Haijie Gu and John D. Lafferty,
Proceedings of the 29th International Conference on Machine
Learning, ICML 2012, Edinburgh, Scotland.
[pdf]
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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]
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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]
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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]
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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]
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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]
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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]
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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]
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Time varying undirected graphs
Shuheng Zhou, John Lafferty and Larry Wasserman
Machine Learning,
Volume 80, Numbers 2-3, September 2010, pp. 295-319.
[link]
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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]
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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]
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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]
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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]
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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]
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Visualizing topics with multiword expressions
David Blei and John Lafferty
[arXiv:0907.1013]
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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]
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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]
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Rodeo: sparse, greedy nonparametric regression
John Lafferty and Larry Wasserman
The Annals of Statistics, Vol. 36, No.1, 2008, pages 28-63
[pdf]
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Comments on: Nonparametric inference with generalized likelihood tests: Nonparametric sparsity
John Lafferty and Larry Wasserman
Test, Vol. 16, pp. 453–455, 2007.
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SpAM: Sparse additive models
Pradeep Ravikumar, Han Liu, John Lafferty and Larry Wasserman
In Advances in Neural Information Processing Systems (NIPS),
20, 2007
[pdf]
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Statistical analysis of semisupervised regression
John Lafferty and Larry Wasserman
In Advances in Neural Information Processing Systems (NIPS),
20, 2007
[pdf]
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Compressed regression
Shuheng Zhou, John Lafferty and Larry Wasserman
In Advances in Neural Information Processing Systems (NIPS),
20, 2007
[pdf]
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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]
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A correlated topic model of Science
David Blei and John Lafferty
Annals of Applied Statistics, Vol. 1, No. 1, 17-35, 2007
[pdf]