94 citations · 361 across the 20 of their papers we have counts for
7 papers · 1 filter
High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods
Christopher C. Johnson, Ali Jalali, Pradeep Ravikumar
In this paper we consider the task of estimating the non-zero pattern of the sparse inverse covariance matrix of a zero-mean Gaussian random vector from a set of iid samples. Note…
Dynamic Batch Bayesian Optimization
Javad Azimi, Ali Jalali, Xiaoli Fern
Bayesian optimization (BO) algorithms try to optimize an unknown function that is expensive to evaluate using minimum number of evaluations/experiments. Most of the proposed algori…
On Learning Discrete Graphical Models Using Greedy Methods
Ali Jalali, Chris Johnson, Pradeep Ravikumar
In this paper, we address the problem of learning the structure of a pairwise graphical model from samples in a high-dimensional setting. Our first main result studies the sparsist…
A Dirty Model for Multiple Sparse Regression
Ali Jalali, Pradeep Ravikumar, Sujay Sanghavi
Sparse linear regression -- finding an unknown vector from linear measurements -- is now known to be possible with fewer samples than variables, via methods like the LASSO. We cons…
Learning the Dependence Graph of Time Series with Latent Factors
Ali Jalali, Sujay Sanghavi
This paper considers the problem of learning, from samples, the dependency structure of a system of linear stochastic differential equations, when some of the variables are latent.…
Clustering Partially Observed Graphs via Convex Optimization
Yudong Chen, Ali Jalali, Sujay Sanghavi +1
This paper considers the problem of clustering a partially observed unweighted graph---i.e., one where for some node pairs we know there is an edge between them, for some others we…
Low-rank Matrix Recovery from Errors and Erasures
Yudong Chen, Ali Jalali, Sujay Sanghavi +1
This paper considers the recovery of a low-rank matrix from an observed version that simultaneously contains both (a) erasures: most entries are not observed, and (b) errors: value…