5 papers
Differentiable Combinatorial Auction Design: Optimizing Revenue-Welfare-Fairness Frontiers With Graph Attention Networks
Mai Pham, Vikrant Vaze, Peter Chin
Designing optimal auctions for allocating public infrastructure-such as airport slots and energy resources-is challenging due to interdependent resources and complex side-constrain…
L2R-EV: Learning What to Repair in Electric Ride-Pooling with Finite Charger Queues
Mai Pham, Vikrant S. Vaze, Peter Chin
Electric ride-pooling must jointly manage passenger service, routes, batteries, and finite chargers; a locally useful relocation can reduce later service. We introduce a discrete-e…
Optimistic Policy Regularization: Elite-Statistic Reward Shaping
Mai Pham, Vikrant Vaze, Peter Chin
On-policy agents discard trajectories after each update, including rare high-return episodes. We propose Optimistic Policy Regularization (OPR), which turns those episodes into a c…
Strategic Cyber Defense via Reinforcement Learning-Guided Combinatorial Auctions
Mai Pham, Vikrant Vaze, Peter Chin
Cyber defense operations increasingly require long-term strategic planning under uncertainty and resource constraints. We propose a new use of combinatorial auctions for allocating…
Advancing Differentiable Economics: A Neural Network Framework for Revenue-Maximizing Combinatorial Auction Mechanisms
Mai Pham, Vikrant Vaze, Peter Chin
Differentiable economics, which uses neural networks as function approximators and gradient-based optimization in automated mechanism design (AMD), marked a significant breakthroug…