4 ICML

Sparse + Group-Sparse Dirty Models: Statistical Guarantees without Unreasonable Conditions and a Case for Non-Convexity

ICML 2017 Sparse + Group-Sparse Dirty Models: Statistical Guarantees without Unreasonable Conditions and a Case for Non-ConvexityEunho Yang, A. Lozano Imposing sparse + group-sparse superposition structures in high-dimensional parameter estimation is known to provide flexible regularizatio...

3 ICML

Ordinal Graphical Models: A Tale of Two Approaches

ICML 2017 Ordinal Graphical Models: A Tale of Two ApproachesArun Sai Suggala, Eunho Yang, Pradeep Ravikumar Undirected graphical models or Markov random fields (MRFs) are widely used for modeling multivariate probability distributions. Much of the work on MRFs has focused on continuous var...

2 ICML

Graphical Models for Ordinal Data: A Tale of Two Approaches

ICML 2017 Graphical Models for Ordinal Data: A Tale of Two ApproachesArun Sai Suggala, Eunho Yang, P. Ravikumar Undirected graphical models or Markov random fields (MRFs) are widely used for modeling multivariate probability distributions. Much of the work on MRFs has focused on continuous...

1 ICML

Combined Group and Exclusive Sparsity for Deep Neural Networks

ICML 2017 Combined Group and Exclusive Sparsity for Deep Neural NetworksJaehong Yoon, SungJu Hwang The number of parameters in a deep neural network is usually very large, which helps with its learning capacity but also hinders its scalability and practicality due to memory/time inefficien...