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Noisy user preference predictions can flip proxy preferences and destabilize ranking scores, but DrEM effectively mitigates this issue, leading to more reliable video recommendations.
Zhinv slashes wind field reconstruction errors by 66%, enabling real-time assessments directly from local observations.
UAME transforms the way we handle user satisfaction in video recommendations by incorporating uncertainty directly into the optimization process, yielding superior alignment with actual user preferences.
Multi-agent RL agents can learn to collaborate *faster* by actively "perceiving" and aligning with each other's policy updates, rather than passively observing environment interactions.
Second-order federated learning can be made robust and practical: FedRCO overcomes instability issues and outperforms first-order methods in non-IID settings.
