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Found 7 papers, 1 papers with code
PAPER-HILT: Personalized and Adaptive Privacy-Aware Early-Exit for Reinforcement Learning in Human-in-the-Loop Systems
Reinforcement Learning (RL) has increasingly become a preferred method over traditional rule-based systems in diverse human-in-the-loop (HITL) applications due to its adaptability to the dynamic nature of human interactions.
FinA: Fairness of Adverse Effects in Decision-Making of Human-Cyber-Physical-System
Ensuring fairness in decision-making systems within Human-Cyber-Physical-Systems (HCPS) is a pressing concern, particularly when diverse individuals, each with varying behaviors and expectations, coexist within the same application space, influenced by a shared set of control actions in the system.
FAIRO: Fairness-aware Adaptation in Sequential-Decision Making for Human-in-the-Loop Systems
Furthermore, we recognize that fairness-aware policies can sometimes conflict with the application's utility.
adaPARL: Adaptive Privacy-Aware Reinforcement Learning for Sequential-Decision Making Human-in-the-Loop Systems
On the one hand, RL methods enhance the user experience by trying to adapt to the highly dynamic nature of humans.
ERUDITE: Human-in-the-Loop IoT for an Adaptive Personalized Learning System
Thanks to the rapid growth in wearable technologies and recent advancement in machine learning and signal processing, monitoring complex human contexts becomes feasible, paving the way to develop human-in-the-loop IoT systems that naturally evolve to adapt to the human and environment state autonomously.
AutoFR: Automated Filter Rule Generation for Adblocking
AutoFR is effective: it generates filter rules that can block 86% of the ads, as compared to 87% by EasyList, while achieving comparable visual breakage.
FaiR-IoT: Fairness-aware Human-in-the-Loop Reinforcement Learning for Harnessing Human Variability in Personalized IoT
Results obtained on these two applications validate the generality of FaiR-IoT and its ability to provide a personalized experience while enhancing the system's performance by 40%-60% compared to non-personalized systems and enhancing the fairness of the multi-human systems by 1. 5 orders of magnitude.
