activity
20122017
most citedIdentifying Best Interventions through Online Importance Sampling

20 citations · 23 across the 5 of their papers we have counts for

collaborators

5 papers

stat.ML201720 cited

Identifying Best Interventions through Online Importance Sampling

Rajat Sen, Karthikeyan Shanmugam, Alexandros G. Dimakis +1

Motivated by applications in computational advertising and systems biology, we consider the problem of identifying the best out of several possible soft interventions at a source n…

cs.NI20142 cited

Serving Content with Unknown Demand:the High-Dimensional Regime

Sharayu Moharir, Javad Ghaderi, Sujay Sanghavi +1

In this paper we look at content placement in the high-dimensional regime: there are n servers, and O(n) distinct types of content. Each server can store and serve O(1) types at an…

cs.IT2014

Wireless Scheduling with Partial Channel State Information: Large Deviations and Optimality

Aditya Gopalan, Constantine Caramanis, Sanjay Shakkottai

We consider a server serving a time-slotted queued system of multiple packet-based flows, with exogenous packet arrivals and time-varying service rates. At each time, the server ca…

cs.NI2012

On the Effect of Channel Fading on Greedy Scheduling

Akula Aneesh Reddy, Sujay Sanghavi, Sanjay Shakkottai

Greedy Maximal Scheduling (GMS) is an attractive low-complexity scheme for scheduling in wireless networks. Recent work has characterized its throughput for the case when there is…

stat.ML20121 cited

Greedy Learning of Markov Network Structure

Praneeth Netrapalli, Siddhartha Banerjee, Sujay Sanghavi +1

We propose a new yet natural algorithm for learning the graph structure of general discrete graphical models (a.k.a. Markov random fields) from samples. Our algorithm finds the nei…