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My research focus is on learning representations where we can provably identify/recover the latent factors from observations, which falls into the area of disentanglement/object-centric learning or more broadly causal representation learning. I also work on treatment effect estimation (causal inference) and trustworthy machine learning, where I have specifically tackled problems in out-of-distribution generalization, privacy robustness, and explainability of machine learning models.
Research Interests
Papers共 13 篇Author StatisticsCo-AuthorSimilar Experts
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ICML 2023pp.18171-18206, (2023)
Conference on Causal Learning and Reasoning (CLeaR) (2022): 19-43
arXiv (Cornell University) (2022)
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139 (2021): 7313-7324
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AIES '21: PROCEEDINGS OF THE 2021 AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETYpp.652-663, (2021)
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