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Long-term Fairness For Real-time Decision Making: A Constrained Online Optimization Approach
However, the widespread integration of machine learning also makes it necessary to ensure machine learning-driven decision-making systems do not violate ethical principles and values of society in which they operate.
Change Point Detection Approach for Online Control of Unknown Time Varying Dynamical Systems
We propose a novel change point detection approach for online learning control with full information feedback (state, disturbance, and cost feedback) for unknown time-varying dynamical systems.
CAFE: Catastrophic Data Leakage in Federated Learning
In this paper, we revisit this defense premise and propose an advanced data leakage attack to efficiently recover batch data from the shared aggregated gradients.
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