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School of Artificial Intelligence, Nanjing University, National Key Laboratory for Novel Software Technology
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Achieving optimal regret in generalized linear bandits with heavy-tailed noise using a novel one-pass update algorithm could revolutionize decision-making in dynamic environments.
You don't need a full causal graph to avoid undesired outcomes; learning a simple order structure can be enough, and even outperform methods that try to learn the whole graph.
