Priority User
"Priority user" research spans diverse fields, focusing on optimizing systems to preferentially serve or allocate resources to designated high-value entities. Current research emphasizes developing algorithms and models, such as multi-agent reinforcement learning (MARL) and deep deterministic policy gradients (DDPG), to dynamically assign priorities and manage resource allocation efficiently, particularly in complex, dynamic environments. This work is significant for improving performance in various applications, including healthcare, transportation, and communication networks, by ensuring timely and effective service for critical tasks and users.
Papers
XP-MARL: Auxiliary Prioritization in Multi-Agent Reinforcement Learning to Address Non-Stationarity
Jianye Xu, Omar Sobhy, Bassam Alrifaee
How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities
Charlotte Bunne, Yusuf Roohani, Yanay Rosen, Ankit Gupta, Xikun Zhang, Marcel Roed, Theo Alexandrov, Mohammed AlQuraishi, Patricia Brennan, Daniel B. Burkhardt, Andrea Califano, Jonah Cool, Abby F. Dernburg, Kirsty Ewing, Emily B. Fox, Matthias Haury, Amy E. Herr, Eric Horvitz, Patrick D. Hsu, Viren Jain, Gregory R. Johnson, Thomas Kalil, David R. Kelley, Shana O. Kelley, Anna Kreshuk, Tim Mitchison, Stephani Otte, Jay Shendure, Nicholas J. Sofroniew, Fabian Theis, Christina V. Theodoris, Srigokul Upadhyayula, Marc Valer, Bo Wang, Eric Xing, Serena Yeung-Levy, Marinka Zitnik, Theofanis Karaletsos, Aviv Regev, Emma Lundberg, Jure Leskovec, Stephen R. Quake