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My research centers around developing novel machine learning algorithms that an generalize well to changing environments. In my research, I focus on two key ingredients: credit assignment and causal learning. These two ingredients flow into and reinforce each other: Appropriate credit assignment can help a model refine itself only at relevant causal variables, while a model that comprehends causality sufficiently well can reason about the connections between causal variables and the effect of interveningon them. Such an improved model can adapt quickly to interventions, thereby avoiding a huge class of errors that impede systematic generalization, particularly out of distribution.
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ICLR 2023 (2022)
Conference on Causal Learning and Reasoning (CLeaR)pp.390-406, (2022)
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ICLR 2023 (2022)
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