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Found 8 papers, 2 papers with code
Limited Resource Allocation in a Non-Markovian World: The Case of Maternal and Child Healthcare
The success of many healthcare programs depends on participants' adherence.
A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning
Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model.
AI-driven Prices for Externalities and Sustainability in Production Markets
Traditional competitive markets do not account for negative externalities; indirect costs that some participants impose on others, such as the cost of over-appropriating a common-pool resource (which diminishes future stock, and thus harvest, for everyone).
Improving Multi-agent Coordination by Learning to Estimate Contention
We present a multi-agent learning algorithm, ALMA-Learning, for efficient and fair allocations in large-scale systems.
Improved Cooperation by Exploiting a Common Signal
Inspired by human behavior, we investigate the learning dynamics and emergence of temporal conventions, focusing on common-pool resources.
A Distributed Differentially Private Algorithm for Resource Allocation in Unboundedly Large Settings
We introduce a practical and scalable algorithm (PALMA) for solving one of the fundamental problems of multi-agent systems -- finding matches and allocations -- in unboundedly large settings (e. g., resource allocation in urban environments, mobility-on-demand systems, etc.
Putting Ridesharing to the Test: Efficient and Scalable Solutions and the Power of Dynamic Vehicle Relocation
We study the optimization of large-scale, real-time ridesharing systems and propose a modular design methodology, Component Algorithms for Ridesharing (CAR).
Anytime Heuristic for Weighted Matching Through Altruism-Inspired Behavior
We present a novel anytime heuristic (ALMA), inspired by the human principle of altruism, for solving the assignment problem.
