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Nov 22, 2024
3:30 PM - 4:30 PM
Speaker Rayadurgam Srikant Abstract We consider policy optimization methods in reinforcement learning settings where the state space is arbitrarily large, or even countably infinite. The motivation arises from control problems in communication networks, matching markets, and other queueing systems. Specifically, we consider the popular Natural Policy Gradient (NPG) algorithm, which has been studied in the past only under the assumption that the cost is bounded and the state space is finit...
Dec 6, 2024
3:30 PM - 4:30 PM
Speakers Arjun Arunasalam Title: Abstract: Bio Matthew DeVerna Title: Fact-checking information from large language models can decrease headline discernment Abstract: Fact checking can be an effective strategy against misinformation, but its implementation at scale is impeded by the overwhelming volume of information online. Recent artificial intelligence (AI) language models have shown impressive ability in fact-checking tasks, but how humans int...