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Creators/Authors contains: "Honavar, V"

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  1. ; ( , Proceedings of The 35th Uncertainty in Artificial Intelligence Conference)
    Accepted Manuscript Full Text Available
  2. ; ( , Proceedings of Machine Learning Research)
    null (Ed.)
    We consider the problem of learning causal relationships from relational data. Existing approaches rely on queries to a relational conditional independence (RCI) oracle to establish and orient causal relations in such a setting. In practice, queries to a RCI oracle have to be replaced by reliable tests for RCI against available data. Relational data present several unique challenges in testing for RCI. We study the conditions under which traditional iid-based CI tests yield reliable answers to RCI queries against relational data. We show how to con- duct CI tests against relational data to robustly recover the underlying relational causal structure. Results of our experiments demonstrate the effectiveness of our proposed approach. 
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    Accepted Manuscript Full Text Available
  3. ; ; ( , Proceedings of the 33rd AAAI Conference on Artificial Intelligence.)
    Accepted Manuscript Full Text Available
  4. ; ; ; ( , Intelligent Data Engineering and Automated Learning)
    Accepted Manuscript Full Text Available
  5. ; ; ; ( , Proceedings of the Eighth IEEE ICDM Workshop on Data Mining in Networks, IEEE.)
    Accepted Manuscript Full Text Available