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Creators/Authors contains: "Yu, Yaoliang"

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  1. ; ; ; ; (, 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE))
    Full Text Available 
  2. ; ; ; (, International Conference on Machine Learning (ICML))
    Invariance (defined in a general sense) has been one of the most effective priors for representation learning. Direct factorization of parametric models is feasible only for a small range of invariances, while regularization approaches, despite improved generality, lead to nonconvex optimization. In this work, we develop a convex representation learning algorithm for a variety of generalized invariances that can be modeled as semi-norms. Novel Euclidean embeddings are introduced for kernel representers in a semi-inner-product space, and approximation bounds are established. This allows invariant representations to be learned efficiently and effectively as confirmed in our experiments, along with accurate predictions. 
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    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; ; ; (, Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering)
    null (Ed.)
    Full Text Available 
  4. ; ; ; ; ; ; ; ; (, Advances in Neural Information Processing Systems 34 (NeurIPS 2021))
    Accepted Manuscript Full Text Available