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Featured researches published by Rajat Sen.


measurement and modeling of computer systems | 2015

Detecting Sponsored Recommendations

Subhashini Krishnasamy; Rajat Sen; Sewoong Oh; Sanjay Shakkottai

Personalized recommender systems provide great opportunities for targeted advertisements, by displaying ads alongside genuine recommendations. We consider a biased recommendation system where such ads are displayed without any tags (disguised as genuine recommendations), rendering them indistinguishable to users. We consider the problem of detecting such a bias and propose an algorithm that uses statistical analysis based on binary feedback data from a subset of users. We prove that the proposed algorithm detects bias with high probability for a broad class of recommendation systems with sufficient number of feedback samples.


Photonic Network Communications | 2017

Comprehensive design methodology for control and data planes in wavelength-routed optical networks

Gitanjali Chandwani; Rajat Sen; Debasish Datta

We propose a comprehensive design methodology for control and data planes of wavelength-routed optical networks (WRONs) employing mixed-line-rate (MLR) transmission for cost-effective resource provisioning. The proposed approach attempts to minimize the maximum lightpath capacity demand in Gbps (representing the measure of lightpath congestion) in network for a given traffic matrix by using a mix of a heuristic scheme and linear programming (LP). In the first step of the proposed three-step design, some lightpaths are set up on a set of judiciously selected fiber links (with point-to-point lightpaths between neighboring nodes), on a specific wavelength throughout the network, and an appropriate fraction of the same set of lightpaths is utilized for carrying control information, forming therefore the control plane (CP) of the WRON. The remaining bandwidth of these lightpaths is utilized to carry the data traffic along with all other designed lightpaths of the WRON using appropriate algorithm, forming the overall data plane (DP) of the WRON. In the second step, traffic routing is carried out through LP to minimize lightpath congestion in the network. In the third step, we utilize the results of LP to assign rates to lightpaths, such that the cost (considering only the transceiver cost) of the network is minimized. This design leads to congestion-aware MLR network with due consideration to cost-effectiveness without compromising the network restoration response against link failures. We carry out simulation studies employing possible CPs using both symmetric (CP topology being same as the physical topology) as well as asymmetric (using fewer fiber links than the symmetric case) topology. The results of our simulations indicate that the proposed design of CP with symmetric/asymmetric topology and in-band transmission with sub-lightpath capacity can bring down network congestion and cost with respect to symmetric out-of-band transmission (using fully reserved lightpaths for CP), without any perceptible sacrifice in respect of the network restoration time. Failure can occur either in CP or DP, or in both the planes. We investigate the effect of design of CP with symmetric/asymmetric topology on network restoration time for single- and double-link failures. We further present DP design methodology with hybrid restoration scheme, i.e., combination of dedicated (1:1) path protection and path restoration. We analyze the effect of symmetric CP topology and degree of protection on the congestion of the network. Some lightpaths, that support more traffic, are protected against failures, while the others are left for path restoration in the event of failures. As more lightpaths are protected, the congestion and power consumption of network increase. We provide an analysis of the factors that come into play while altering the degree of protection and observe how the choice for the degree of protection in DP can be arrived at using an appropriate design methodology.


neural information processing systems | 2016

Regret of Queueing Bandits

Subhashini Krishnasamy; Rajat Sen; Ramesh Johari; Sanjay Shakkottai


international conference on machine learning | 2017

Identifying Best Interventions through Online Importance Sampling

Rajat Sen; Karthikeyan Shanmugam; Alexandros G. Dimakis; Sanjay Shakkottai


international conference on artificial intelligence and statistics | 2017

Contextual Bandits with Latent Confounders: An NMF Approach.

Rajat Sen; Karthikeyan Shanmugam; Murat Kocaoglu; Alexandros G. Dimakis; Sanjay Shakkottai


arXiv: Learning | 2016

Latent Contextual Bandits: A Non-Negative Matrix Factorization Approach.

Rajat Sen; Karthikeyan Shanmugam; Murat Kocaoglu; Alexandros G. Dimakis; Sanjay Shakkottai


MAPAN | 2015

Comparison Between Three Tuning Methods of PID Control for High Precision Positioning Stage

Rajat Sen; Chinmoy Pati; Samik Dutta; Ranjan Sen


international conference on machine learning | 2018

Multi-Fidelity Black-Box Optimization with Hierarchical Partitions

Rajat Sen; Kirthevasan Kandasamy; Sanjay Shakkottai


international conference on artificial intelligence and statistics | 2018

Contextual Bandits with Stochastic Experts.

Rajat Sen; Karthikeyan Shanmugam; Sanjay Shakkottai


arXiv: Systems and Control | 2018

Learning Unknown Service Rates in Queues: A Multi-Armed Bandit Approach.

Subhashini Krishnasamy; Rajat Sen; Ramesh Johari; Sanjay Shakkottai

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Sanjay Shakkottai

University of Texas at Austin

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Karthikeyan Shanmugam

University of Texas at Austin

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Alexandros G. Dimakis

University of Texas at Austin

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Ethan R. Elenberg

University of Texas at Austin

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