Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models

On rings of operators. reduction theory

Von Neumann, J. (1949) · 1949

Earlier work this paper cites.

Rademacher and gaussian complexities: Risk bounds and structural results

Bartlett, P. L. and Mendelson, S. (2002) · 2002

Earlier work this paper cites.

An interior-point method for large-scale ℓ 1 \ell_{1} -regularized logistic regression

Koh, K., Kim, S.-J., and Boyd, S. (2007) · 2007

Earlier work this paper cites.

Efficient structure learning of markov networks using l ​ _ ​ 1 l\_1 -regularization

Lee, S.-I., Ganapathi, V., and Koller, D. (2007) · 2007

Earlier work this paper cites.

Model selection and estimation in the gaussian graphical model

Original

Yuan, M. and Lin, Y. (2007) · 2007

Earlier work this paper cites.

Model selection through sparse maximum likelihood estimation for multivariate gaussian or binary data

Banerjee, O., Ghaoui, L. E., and d’Aspremont, A. (2008) · 2008

Earlier work this paper cites.

Which graphical models are difficult to learn?

Bento, J. and Montanari, A. (2009) · 2009

Earlier work this paper cites.

Inferring friendship network structure by using mobile phone data

Eagle, N., Pentland, A. S., and Lazer, D. (2009) · 2009

Earlier work this paper cites.

On the complexity of linear prediction: Risk bounds, margin bounds, and regularization

Kakade, S. M., Sridharan, K., and Tewari, A. (2009) · 2009

Earlier work this paper cites.

Fast global convergence rates of gradient methods for high-dimensional statistical recovery

Agarwal, A., Negahban, S., and Wainwright, M. J. (2010) · 2010

Earlier work this paper cites.

Exploiting hierarchical context on a large database of object categories

Choi, M. J., Lim, J. J., Torralba, A., and Willsky, A. S. (2010) · 2010

Cited alongside, same era.

High-dimensional ising model selection using ℓ 1 \ell_{1} -regularized logistic regression

Ravikumar, P., Wainwright, M. J., and Lafferty, J. D. (2010) · 2010

Cited alongside, same era.

On learning discrete graphical models using group-sparse regularization

Jalali, A., Ravikumar, P., Vasuki, V., and Sanghavi, S. (2011) · 2011

Cited alongside, same era.

Inverse ising inference using all the data

Aurell, E. and Ekeberg, M. (2012) · 2012

Cited alongside, same era.

Regularization techniques for learning with matrices

Kakade, S. M., Shalev-Shwartz, S., and Tewari, A. (2012) · 2012

Cited alongside, same era.

Wisdom of crowds for robust gene network inference

Marbach, D., Costello, J. C., Küffner, R., Vega, N. M., Prill, R. J., Camacho, D. M., Allison, K. R., Consortium, T. D., Kellis, M., Collins, J. J., and Stolovitzky, G. (2012) · 2012

Lectures on modern convex optimization

Ben-Tal, A. and Nemirovski, A. (Fall 2013) · 2013

Later among the works it cites.

Understanding machine learning: From theory to algorithms

Shalev-Shwartz, S. and Ben-David, S. (2014) · 2014

Later among the works it cites.

Efficiently learning ising models on arbitrary graphs

Bresler, G. (2015) · 2015

Later among the works it cites.

Convex optimization: Algorithms and complexity

Bubeck, S. (2015) · 2015

Later among the works it cites.

Interaction screening: Efficient and sample-optimal learning of ising models

Vuffray, M., Misra, S., Lokhov, A., and Chertkov, M. (2016) · 2016

Later among the works it cites.

Information theoretic properties of markov random fields, and their algorithmic applications

Hamilton, L., Koehler, F., and Moitra, A. (2017) · 2017

Later among the works it cites.