PAPER-HILT: Personalized and Adaptive Privacy-Aware Early-Exit for Reinforcement Learning in Human-in-the-Loop Systems

no code implementations • 9 Mar 2024 • Mojtaba Taherisadr, Salma Elmalaki

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.

Reinforcement Learning (RL)

FAIRO: Fairness-aware Adaptation in Sequential-Decision Making for Human-in-the-Loop Systems

no code implementations • 12 Jul 2023 • Tianyu Zhao, Mojtaba Taherisadr, Salma Elmalaki

Furthermore, we recognize that fairness-aware policies can sometimes conflict with the application's utility.

Decision Making Fairness

adaPARL: Adaptive Privacy-Aware Reinforcement Learning for Sequential-Decision Making Human-in-the-Loop Systems

no code implementations • 7 Mar 2023 • Mojtaba Taherisadr, Stelios Andrew Stavroulakis, Salma Elmalaki

On the one hand, RL methods enhance the user experience by trying to adapt to the highly dynamic nature of humans.

Decision Making Reinforcement Learning (RL)

ERUDITE: Human-in-the-Loop IoT for an Adaptive Personalized Learning System

no code implementations • 7 Mar 2023 • Mojtaba Taherisadr, Mohammad Abdullah Al Faruque, Salma Elmalaki

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.

Learning Theory