194 citations · 730 across the 35 of their papers we have counts for
6 papers · 1 filter
Score Function Features for Discriminative Learning: Matrix and Tensor Framework
Majid Janzamin, Hanie Sedghi, Anima Anandkumar
Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we cons…
Score Function Features for Discriminative Learning
Majid Janzamin, Hanie Sedghi, Anima Anandkumar
Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we cons…
Provable Tensor Methods for Learning Mixtures of Generalized Linear Models
Hanie Sedghi, Majid Janzamin, Anima Anandkumar
We consider the problem of learning mixtures of generalized linear models (GLM) which arise in classification and regression problems. Typical learning approaches such as expectati…
Provable Methods for Training Neural Networks with Sparse Connectivity
Hanie Sedghi, Anima Anandkumar
We provide novel guaranteed approaches for training feedforward neural networks with sparse connectivity. We leverage on the techniques developed previously for learning linear net…
Statistical Structure Learning, Towards a Robust Smart Grid
Hanie Sedghi, Edmond Jonckheere
Robust control and maintenance of the grid relies on accurate data. Both PMUs and state estimators are prone to false data injection attacks. Thus, it is crucial to have a mechanis…
Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Noisy Matrix Decomposition
Hanie Sedghi, Anima Anandkumar, Edmond Jonckheere
We propose an efficient ADMM method with guarantees for high-dimensional problems. We provide explicit bounds for the sparse optimization problem and the noisy matrix decomposition…