3 citations · 3 across the 7 of their papers we have counts for
9 papers
A Recommendation System Approach for Interference-Robust Sensor Subset Selection
Kaan Buyukkalayci, Kyle Pak, Merve Karakas +1
This paper develops a method for sensor-subset selection for tracking. Prior work showed that low-cost acoustic Received Signal Strength Indicator (RSSI) measurements can be used t…
Multi-ResNets for Subspace Preconditioning in Constrained Optimization
Merve Karakas, Christopher J. Williams, Emmanuel O. Balogun +3
We propose MResOpt, a staged residual neural network architecture for constrained optimization problems. Our architecture fits within predict-complete-correct pipelines and decompo…
Best-Arm Identification with Noisy Actuation
Merve Karakas, Osama Hanna, Lin F. Yang +1
In this paper, we consider a multi-armed bandit (MAB) instance and study how to identify the best arm when arm commands are conveyed from a central learner to a distributed agent o…
Top-P Sensor Selection for Target Localization
Kaan Buyukkalayci, Kyle Pak, Merve Karakas +2
We study set-valued decision rules in which performance is defined by the inclusion of the top- hypotheses, rather than only the single best or true hypothesis. This criterion i…
Enhancing Binary Search via Overlapping Partitions
Kaan Buyukkalayci, Merve Karakas, Xinlin Li +1
This paper considers the task of performing binary search under noisy decisions, focusing on the application of target area localization. In the presence of noise, the classical pa…
Does Feedback Help in Bandits with Arm Erasures?
Merve Karakas, Osama Hanna, Lin F. Yang +1
We study a distributed multi-armed bandit (MAB) problem over arm erasure channels, motivated by the increasing adoption of MAB algorithms over communication-constrained networks. I…
Learning for Bandits under Action Erasures
Osama Hanna, Merve Karakas, Lin F. Yang +1
We consider a novel multi-arm bandit (MAB) setup, where a learner needs to communicate the actions to distributed agents over erasure channels, while the rewards for the actions ar…
Multi-Agent Bandit Learning through Heterogeneous Action Erasure Channels
Osama A. Hanna, Merve Karakas, Lin F. Yang +1
Multi-Armed Bandit (MAB) systems are witnessing an upswing in applications within multi-agent distributed environments, leading to the advancement of collaborative MAB algorithms.…
Vehicular Visible Light Positioning for Collision Avoidance and Platooning: A Survey
Burak Soner, Merve Karakas, Utku Noyan +2
Relative vehicle positioning methods can contribute to safer and more efficient autonomous driving by enabling collision avoidance and platooning applications. For full automation,…