Results for stat.ML

Publications & Outputs

1. Scalable Monte Carlo for Bayesian Learning

   Fearnhead, P., Nemeth, C., Oates, C. J. & Sherlock, C., 5/05/2025, Cambridge: Cambridge University Press. ( Institute of Mathematical Statistics Monographs)

   Research output: Book/Report/Proceedings › Book

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., 31/12/2024, In: Proceedings of Machine Learning Research. 235, p. 39556-39586 31 p.

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

3. Federated $\mathcal{X}$-armed Bandit with Flexible Personalisation

   Arabzadeh, A., Grant, J. A. & Leslie, D. S., 11/09/2024, Arxiv.

   Research output: Working paper › Preprint

4. 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

5. 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

6. 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

7. Learning Rate Free Bayesian Inference in Constrained Domains

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

   Research output: Working paper › Preprint

8. 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

9. Gaussian Processes on Hypergraphs

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

   Research output: Working paper › Preprint

10. Efficient and Generalizable Tuning Strategies for Stochastic Gradient MCMC

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

    Research output: Working paper › Preprint

11. 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

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