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Learning Payment-Free Resource Allocation Mechanisms
We consider the design of mechanisms that allocate limited resources among self-interested agents using neural networks.
Sequential Fair Resource Allocation under a Markov Decision Process Framework
We propose a new algorithm, SAFFE, that makes fair allocations with respect to the entire demands revealed over the horizon by accounting for expected future demands at each arrival time.
Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems
Communication is important in many multi-agent reinforcement learning (MARL) problems for agents to share information and make good decisions.
Generative Models with Information-Theoretic Protection Against Membership Inference Attacks
Deep generative models, such as Generative Adversarial Networks (GANs), synthesize diverse high-fidelity data samples by estimating the underlying distribution of high dimensional data.
Optimal Admission Control for Multiclass Queues with Time-Varying Arrival Rates via State Abstraction
To improve the scalability of our approach to a greater number of task classes, we present an approximation based on state abstraction.
Tradeoffs in Streaming Binary Classification under Limited Inspection Resources
Institutions are increasingly relying on machine learning models to identify and alert on abnormal events, such as fraud, cyber attacks and system failures.
Non-Parametric Stochastic Sequential Assignment With Random Arrival Times
We consider a problem wherein jobs arrive at random times and assume random values.
