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Publications & Outputs

  1. Semi-Supervised Learning guided by the Generalized Bayes Rule under Soft Revision

    Dietrich, S., Rodemann, J. & Jansen, C., 24/05/2024, Arxiv.

    Research output: Working paper › Preprint

  2. Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

    Papamarkou, T., Skoularidou, M., Palla, K., Aitchison, L., Arbel, J., Dunson, D., Filippone, M., Fortuin, V., Hennig, P., Hernández-Lobato, J. M., Hubin, A., Immer, A., Karaletsos, T., Khan, M. E., Kristiadi, A., Li, Y., Mandt, S., Nemeth, C., Osborne, M. A. & Rudner, T. G. J. & 5 others, Rügamer, D., Teh, Y. W., Welling, M., Wilson, A. G. & Zhang, R., 1/05/2024, (Accepted/In press) In: Proceedings of Machine Learning Research.

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

  3. Reversible Jump PDMP Samplers for Variable Selection

    Chevallier, A., Fearnhead, P. & Sutton, M., 31/07/2023, In: Journal of the American Statistical Association. 118, 544, p. 2915-2927

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

  4. CoinEM: Tuning-Free Particle-Based Variational Inference for Latent Variable Models

    Sharrock, L., Dodd, D. & Nemeth, C., 24/05/2023.

    Research output: Working paper › Preprint

  5. Learning Rate Free Bayesian Inference in Constrained Domains

    Sharrock, L., Mackey, L. & Nemeth, C., 24/05/2023.

    Research output: Working paper › Preprint

  6. SwISS: A Scalable Markov chain Monte Carlo Divide-and-Conquer Strategy

    Vyner, C., Nemeth, C. & Sherlock, C., 8/08/2022.

    Research output: Working paper › Preprint

  7. Gaussian Processes on Hypergraphs

    Pinder, T., Turnbull, K., Nemeth, C. & Leslie, D., 3/06/2021.

    Research output: Working paper › Preprint

  8. Efficient and Generalizable Tuning Strategies for Stochastic Gradient MCMC

    Coullon, J., South, L. & Nemeth, C., 27/05/2021, Arxiv.

    Research output: Working paper › Preprint

  9. Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling

    Eide, S., Leslie, D. S. & Frigessi, A., 30/04/2021, In: arxiv.org.

    Research output: Contribution to Journal/Magazine › Journal article

  10. Stein Variational Gaussian Processes

    Pinder, T., Nemeth, C. & Leslie, D., 25/09/2020, In: arXiv.

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

  11. MUMBO: MUlti-task Max-value Bayesian Optimization

    Moss, H. B., Leslie, D. S. & Rayson, P., 14/09/2020, achine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Proceedings. Springer, p. 447-462 16 p.

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

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