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Creators/Authors contains: "Yang, Carl"

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  1. ; ; ; ( , 46th International ACM SIGIR Conference on Research and Development in Information Retrieval)
    Free, publicly-accessible full text available January 1, 2024
  2. ; ; ; ; ; ; ; ( , Thirty-Seventh AAAI Conference on Artificial Intelligence)
    Free, publicly-accessible full text available January 1, 2024
  3. ; ; ; ; ( , IEEE Transactions on Knowledge and Data Engineering)
    Free, publicly-accessible full text available October 1, 2023
  4. ; ; ; ; ; ; ; ; ; ( , IEEE Transactions on Medical Imaging)
    Free, publicly-accessible full text available November 1, 2023
  5. ; ; ; ( , Advances in neural information processing systems)
    Accepted Manuscript Full Text Available
  6. ; ; ; ; ; ( , Proceedings of the 2021 Annual Conference on Neural Information Processing Systems)
    Accepted Manuscript Full Text Available
  7. ; ; ; ; ; ( , Proceedings of the 2nd Machine Learning for Health symposium)
    Electronic Health Record modeling is crucial for digital medicine. However, existing models ignore higher-order interactions among medical codes and their causal relations towards downstream clinical predictions. To address such limitations, we propose a novel framework CACHE, to provide effective and insightful clinical predictions based on hypergraph representation learning and counterfactual and factual reasoning techniques. Experiments on two real EHR datasets show the superior performance of CACHE. Case studies with a domain expert illustrate a preferred capability of CACHE in generating clinically meaningful interpretations towards the correct predictions.
    Accepted Manuscript Full Text Available
  8. ; ; ( , IJCAI'21 Proc. 13th International Joint Conference on Artificial Intelligence)
    Zhi (Ed.)

    Network representation learning aims at transferring node proximity in networks into distributed vectors, which can be leveraged in various downstream applications. Recent research has shown that nodes in a network can often be organized in latent hierarchical structures, but without a particular underlying taxonomy, the learned node embedding is less useful nor interpretable. In this work, we aim to improve network embedding by modeling the conditional node proximity in networks indicated by node labels residing in real taxonomies. In the meantime, we also aim to model the hierarchical label proximity in the given taxonomies, which is too coarse by solely looking at the hierarchical topologies. Comprehensive experiments and case studies demonstrate the utility of TAXOGAN.

    Full Text Available 
  9. ; ; ; ; ; ; ; ; ; ( , IEEE transactions on medical imaging)
    Accepted Manuscript Full Text Available
  10. ; ; ; ; ( , The Web Conference 2021)
    Full Text Available