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Creators/Authors contains: "De_Silva, Ashwin"

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  1. ; ; ; ; (, NeurIPS)
    Free, publicly-accessible full text available December 1, 2026
  2. ; ; ; ; ; (, arxiv.org)
    Free, publicly-accessible full text available July 10, 2026
  3. ; ; ; ; ; (, arxiv.org)
    Free, publicly-accessible full text available January 30, 2026
  4. ; ; ; ; (, Mathematics)
    We propose and study a data-driven method that can interpolate between a classical and a modern approach to classification for a class of linear models. The class is the convex combinations of an average of the source task classifiers and a classifier trained on the limited data available for the target task. We derive the expected loss of an element in the class with respect to the target distribution for a specific generative model, propose a computable approximation of the loss, and demonstrate that the element of the proposed class that minimizes the approximated risk is able to exploit a natural bias–variance trade-off in task space in both simulated and real-data settings. We conclude by discussing further applications, limitations, and potential future research directions. 
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  5. ; ; ; ; ; ; ; ; ; ; et al (, Conference on Lifelong Learning Agents (CoLLAs))
    Accepted Manuscript Full Text Available