Search Results for author: Ali Jalali

Found 7 papers, 0 papers with code

Not All Features Are Equal: Discovering Essential Features for Preserving Prediction Privacy

no code implementations26 Mar 2020 Fatemehsadat Mireshghallah, Mohammadkazem Taram, Ali Jalali, Ahmed Taha Elthakeb, Dean Tullsen, Hadi Esmaeilzadeh

We formulate this problem as a gradient-based perturbation maximization method that discovers this subset in the input feature space with respect to the functionality of the prediction model used by the provider.

Scalable Audience Reach Estimation in Real-time Online Advertising

no code implementations14 May 2013 Ali Jalali, Santanu Kolay, Peter Foldes, Ali Dasdan

This trade-off between reachability and performance illustrates a need for a forecasting system that can quickly predict/estimate (with good accuracy) this trade-off.

Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising

no code implementations14 May 2013 Kuang-Chih Lee, Ali Jalali, Ali Dasdan

Today, billions of display ad impressions are purchased on a daily basis through a public auction hosted by real time bidding (RTB) exchanges.

A Lipschitz Exploration-Exploitation Scheme for Bayesian Optimization

no code implementations30 Mar 2012 Ali Jalali, Javad Azimi, Xiaoli Fern, Ruofei Zhang

The exploration phase aims to select samples that shrink the search space as much as possible.

On Learning Discrete Graphical Models using Greedy Methods

no code implementations NeurIPS 2011 Ali Jalali, Christopher C. Johnson, Pradeep K. Ravikumar

In this paper, we address the problem of learning the structure of a pairwise graphical model from samples in a high-dimensional setting.

Clustering Partially Observed Graphs via Convex Optimization

no code implementations25 Apr 2011 Yudong Chen, Ali Jalali, Sujay Sanghavi, Huan Xu

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 know there is no edge, and for the remaining we do not know whether or not there is an edge.

Stochastic Block Model

A Dirty Model for Multi-task Learning

no code implementations NeurIPS 2010 Ali Jalali, Sujay Sanghavi, Chao Ruan, Pradeep K. Ravikumar

However, these papers also caution that the performance of such block-regularized methods are very dependent on the {\em extent} to which the features are shared across tasks.

Multi-Task Learning

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