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Multi-task learning improves ancestral state reconstruction

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  • Ho, Lam Si Tung
  • Dinh, Vu
  • Nguyen, Cuong V.

Abstract

We consider the ancestral state reconstruction problem where we need to infer phenotypes of ancestors using observations from present-day species. For this problem, we propose a multi-task learning method that uses regularized maximum likelihood to estimate the ancestral states of various traits simultaneously. We then show both theoretically and by simulation that this method improves the estimates of the ancestral states compared to the maximum likelihood method. The result also indicates that for the problem of ancestral state reconstruction under the Brownian motion model, the maximum likelihood method can be improved.

Suggested Citation

  • Ho, Lam Si Tung & Dinh, Vu & Nguyen, Cuong V., 2019. "Multi-task learning improves ancestral state reconstruction," Theoretical Population Biology, Elsevier, vol. 126(C), pages 33-39.
    • Handle: RePEc:eee:thpobi:v:126:y:2019:i:c:p:33-39
    DOI: 10.1016/j.tpb.2019.01.001
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    1. Olaf R. P. Bininda-Emonds & Marcel Cardillo & Kate E. Jones & Ross D. E. MacPhee & Robin M. D. Beck & Richard Grenyer & Samantha A. Price & Rutger A. Vos & John L. Gittleman & Andy Purvis, 2007. "The delayed rise of present-day mammals," Nature, Nature, vol. 446(7135), pages 507-512, March.
    2. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
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    Cited by:

    1. Ho, Lam Si Tung & Dinh, Vu, 2022. "When can we reconstruct the ancestral state? A unified theory," Theoretical Population Biology, Elsevier, vol. 148(C), pages 22-27.

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