Anima Anandkumar

This work considers a computationally and statistically efficient parameter estimation method for a wide class of latent variable models--including Gaussian mixture models, hidden Markov models, and latent Dirichlet allocation--which exploits a certain tensor structure in their low-order observable moments. Expand

This work develops a computationally efficient method of moments based on only low-order moments that can be used to estimate the parameters of a broad class of high-dimensional mixture models with many components, including multi-view mixtures of Gaussians. Expand

We propose a new provable method for robust PCA, where the task is to recover a low-rank matrix, which is corrupted with sparse perturbations. Expand

We study the problem of learning a latent tree graphical model where samples are available only from a subset of variables. Expand

We prove that signSGD can get the best of both worlds: compressed gradients and SGD-level convergence rate. Expand

This work provides a simple and efficient learning procedure that is guaranteed to recover the parameters for a wide class of multi-view models and topic models, including latent Dirichlet allocation (LDA). Expand

We propose policies for distributed learning and access which achieve order-optimal cognitive system throughput (number of successful secondary transmissions) under self play, i.e., when implemented at all the secondary users. Expand

Knowledge distillation (KD) consists of transferring knowledge from one machine learning model (the teacher}) to another (the student). Expand

We propose Stochastic Activation Pruning (SAP), a mixed strategy for adversarial defense. Expand

The problem of cooperative allocation among multiple secondary users to maximize cognitive system throughput is considered. Expand