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Research

Dr Henry Moss

Lecturer in Mathematics and AI, Research Student

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  1. 2024
  2. E-pub ahead of print

    GAUCHE: a library for Gaussian processes in chemistry

    Griffiths, R.-R., Klarner, L., Moss, H., Ravuri, A., Truong, S., Du, Y., Stanton, S., Tom, G., Rankovic, B. & Jamasb, A., 9/12/2024, (E-pub ahead of print) NeurIPS Proceedings 2024. 24 p. (Advances in Neural Information Processing Systems; vol. 37).

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

  3. Published

    Bayesian optimisation for additive screening and yield improvements--beyond one-hot encoding

    Ranković, B., Griffiths, R.-R., Moss, H. B. & Schwaller, P., 1/04/2024, In: Digital Discovery. 3, 4, p. 654-666 13 p.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

  4. 2022
  5. Published

    Data-driven discovery of molecular photoswitches with multioutput Gaussian processes

    Griffiths, R.-R., Greenfield, J. L., Thawani, A. R., Jamasb, A. R., Moss, H. B., Bourached, A., Jones, P., McCorkindale, W., Aldrick, A. A. & Fuchter, M. J., 7/12/2022, In: Chemical Science. 13, 45, p. 13541-13551 11 p.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

  6. Published

    Bayesian quantile and expectile optimisation

    Picheny, V., Moss, H., Torossian, L. & Durrande, N., 5/05/2022, Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022),. Cussens, J. & Zhang, K. (eds.). PMLR, p. 1623-1633 11 p.

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

  7. 2021
  8. Published

    Scalable Thompson sampling using sparse Gaussian process models

    Vakili, S., Moss, H., Artemev, A., Dutordoir, V. & Picheny, V., 6/12/2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Vol. 34. p. 5631-5643 13 p. (Advances in Neural Information Processing Systems).

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

  9. Published

    GIBBON: General-purpose Information-Based Bayesian Optimisation

    Moss, H. B., Leslie, D. S., Gonzalez, J. & Rayson, P., 8/10/2021, In: Journal of Machine Learning Research. 22, 235, p. 1-49 49 p.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

  10. Published

    General-purpose Information-theoretical Bayesian Optimisation: A thesis by acronyms

    Moss, H., 2021, Lancaster University. 213 p.

    Research output: Thesis › Doctoral Thesis

  11. 2020
  12. Published

    BOSS: Bayesian Optimization over String Spaces

    Moss, H., Beck, D., Gonzalez, J., Leslie, D. & Rayson, P., 1/12/2020, Advances in Neural Information Processing Systems. 2020 ed. MIT Press, Vol. 2020-December. p. 15476-15486 11 p. (Advances in Neural Information Processing Systems).

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

  13. Published

    MUMBO: MUlti-task Max-value Bayesian Optimization

    Moss, H. B., Leslie, D. S. & Rayson, P., 14/09/2020, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Proceedings. Hutter, F., Kersting, K., Lijffijt, J. & Valera, I. (eds.). Cham: Springer, p. 447-462 16 p. (Lecture Notes in Computer Science; vol. 12459).

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

  14. Published

    BOSH: Bayesian Optimization by Sampling Hierarchically

    Moss, H. B., Leslie, D. S. & Rayson, P., 18/07/2020. 8 p.

    Research output: Contribution to conference - Without ISBN/ISSN › Conference paper

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