Learning to Discover Sparse Graphical Models
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Matthew B. Blaschko | Gaël Varoquaux | Eugene Belilovsky | Kyle Kastner | G. Varoquaux | Kyle Kastner | Eugene Belilovsky
[1] Anders Ellern Bilgrau,et al. Rags2ridges : Ridge estimation of precision matrices from high-dimensional data , 2017 .
[2] Bernhard Schölkopf,et al. Towards a Learning Theory of Causation , 2015, 1502.02398.
[3] A. Dalalyan,et al. On estimation of the diagonal elements of a sparse precision matrix , 2015, 1504.04696.
[4] R. Tibshirani,et al. Sparse inverse covariance estimation with the graphical lasso. , 2008, Biostatistics.
[5] Joan Bruna,et al. Deep Convolutional Networks on Graph-Structured Data , 2015, ArXiv.
[6] Martin J. Wainwright,et al. Information-theoretic bounds on model selection for Gaussian Markov random fields , 2010, 2010 IEEE International Symposium on Information Theory.
[7] M. A. Gómez–Villegas,et al. A MATRIX VARIATE GENERALIZATION OF THE POWER EXPONENTIAL FAMILY OF DISTRIBUTIONS , 2002 .
[8] Masashi Sugiyama,et al. Bayesian Dark Knowledge , 2015 .
[9] Hisayuki Hara,et al. A Localization Approach to Improve Iterative Proportional Scaling in Gaussian Graphical Models , 2008, 0802.2581.
[10] N. Meinshausen,et al. High-dimensional graphs and variable selection with the Lasso , 2006, math/0608017.
[11] Wen Gao,et al. Maximal Sparsity with Deep Networks? , 2016, NIPS.
[12] Olivier Ledoit,et al. A well-conditioned estimator for large-dimensional covariance matrices , 2004 .
[13] Yann LeCun,et al. Learning Fast Approximations of Sparse Coding , 2010, ICML.
[14] Xinyun Chen. Under Review as a Conference Paper at Iclr 2017 Delving into Transferable Adversarial Ex- Amples and Black-box Attacks , 2016 .
[15] Daniel P. Kennedy,et al. The Autism Brain Imaging Data Exchange: Towards Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism , 2013, Molecular Psychiatry.
[16] Gaël Varoquaux,et al. Multi-subject Dictionary Learning to Segment an Atlas of Brain Spontaneous Activity , 2011, IPMI.
[17] Joan Bruna,et al. Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation , 2014, NIPS.
[18] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[19] Nadav Cohen,et al. On the Expressive Power of Deep Learning: A Tensor Analysis , 2015, COLT 2016.
[20] A. Mohammadi,et al. Bayesian Structure Learning in Sparse Gaussian Graphical Models , 2012, 1210.5371.
[21] Patrick Danaher,et al. The joint graphical lasso for inverse covariance estimation across multiple classes , 2011, Journal of the Royal Statistical Society. Series B, Statistical methodology.
[22] Gaël Varoquaux,et al. Learning and comparing functional connectomes across subjects , 2013, NeuroImage.
[23] Michael I. Jordan. Graphical Models , 2003 .
[24] Bin Yu,et al. High-dimensional covariance estimation by minimizing ℓ1-penalized log-determinant divergence , 2008, 0811.3628.
[25] Seungyeop Han,et al. Structured Learning of Gaussian Graphical Models , 2012, NIPS.
[26] Alexandr Andoni,et al. Learning Polynomials with Neural Networks , 2014, ICML.
[27] Jean-Baptiste Poline,et al. Brain covariance selection: better individual functional connectivity models using population prior , 2010, NIPS.
[28] Alex Lenkoski,et al. A direct sampler for G‐Wishart variates , 2013, 1304.1350.
[29] Alán Aspuru-Guzik,et al. Convolutional Networks on Graphs for Learning Molecular Fingerprints , 2015, NIPS.
[30] T. Cai,et al. A Constrained ℓ1 Minimization Approach to Sparse Precision Matrix Estimation , 2011, 1102.2233.
[31] Duncan J. Watts,et al. Collective dynamics of ‘small-world’ networks , 1998, Nature.
[32] Vladlen Koltun,et al. Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.
[33] Raquel Urtasun,et al. Understanding the Effective Receptive Field in Deep Convolutional Neural Networks , 2016, NIPS.
[34] Kaustubh Supekar,et al. Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty , 2012, NeuroImage.
[35] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[36] Matthew B. Blaschko,et al. Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity , 2015, NIPS.
[37] Mohammad Emtiyaz Khan,et al. Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models , 2009, NIPS.
[38] Richard S. Zemel,et al. Gated Graph Sequence Neural Networks , 2015, ICLR.