94 citations · 361 across the 20 of their papers we have counts for
10 papers · 1 filter
Not All Features Are Equal: Discovering Essential Features for Preserving Prediction Privacy
Fatemehsadat Mireshghallah, Mohammadkazem Taram, Ali Jalali +3
When receiving machine learning services from the cloud, the provider does not need to receive all features; in fact, only a subset of the features are necessary for the target pre…
Scalable Audience Reach Estimation in Real-time Online Advertising
Ali Jalali, Santanu Kolay, Peter Foldes +1
Online advertising has been introduced as one of the most efficient methods of advertising throughout the recent years. Yet, advertisers are concerned about the efficiency of their…
A Lipschitz Exploration-Exploitation Scheme for Bayesian Optimization
Ali Jalali, Javad Azimi, Xiaoli Fern +1
The problem of optimizing unknown costly-to-evaluate functions has been studied for a long time in the context of Bayesian Optimization. Algorithms in this field aim to find the op…
Clustering using Max-norm Constrained Optimization
Ali Jalali, Nathan Srebro
We suggest using the max-norm as a convex surrogate constraint for clustering. We show how this yields a better exact cluster recovery guarantee than previously suggested nuclear-n…
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…