3 citations · 3 across the 9 of their papers we have counts for
11 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…
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…
Mixing Makes Markovian Contexts Cheap for Linear Bandits
Kaan Buyukkalayci, Osama Hanna, Christina Fragouli
Recent work shows that when contexts are drawn i.i.d., linear contextual bandits can be reduced to single-context linear bandits. This ``contexts are cheap'' perspective is highly…
ScaleBITS: Scalable Bitwidth Search for Hardware-Aligned Mixed-Precision LLMs
Xinlin Li, Timothy Chou, Josh Fromm +3
Post-training weight quantization is crucial for reducing the memory and inference cost of large language models (LLMs), yet pushing the average precision below 4 bits remains chal…
Two-Level Priority Coding for Resilience to Arbitrary Blockage Patterns
Mine Gokce Dogan, Abhiram Kadiyala, Jaimin Shah +2
Ultra-reliable low-latency communication is essential in mission-critical settings, including military applications, where persistent and asymmetric link blockages caused by mobili…
ICQuant: Index Coding enables Low-bit LLM Quantization
Xinlin Li, Osama Hanna, Christina Fragouli +1
The rapid deployment of Large Language Models (LLMs) highlights the need for efficient low-bit post-training quantization (PTQ), due to their high memory costs. A key challenge in…
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…
Achieving Low Latency at Low Outage: Multilevel Coding for mmWave Channels
Mine Gokce Dogan, Jaimin Shah, Martina Cardone +4
Millimeter-wave (mmWave) spectrum is expected to support data-intensive applications that require ultra-reliable low-latency communications (URLLC). However, mmWave links are highl…