University of Oxford, Department of Statistics logo
University of Oxford, Department of Statistics logo
To Department of Statistics Site

Publications

Jiang, X. et al. (2023) “Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk”, Nature Genetics, 55(11), pp. 1854–1865.
Johnson, E., Pike-Burke, C. and Rebeschini, P. (2023) “Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity”, arXiv.
Alfano, C., Yuan, R. and Rebeschini, P. (2023) “A novel framework for policy mirror descent with general parameterization and linear convergence”, in. Curran Associates.
Hunter, D. and Holmes, C. (2023) “Where medical statistics meets artificial intelligence”, New England Journal of Medicine, 389(13), pp. 1211–1219.
Temčinas, T., Nanda, V. and Reinert, G. (2023) “Goodness-of-fit via Count Statistics in Dense Random Simplicial Complexes”, arXiv.
Lee, H. et al. (2023) “Deep neural networks with dependent weights: Gaussian process mixture limit, heavy tails, sparsity and compressibility”, Journal of Machine Learning Research, 24, pp. 1–78.
Vales, S. et al. (2023) “Discovery and pharmacophoric characterization of chemokine network inhibitors using phage-display, saturation mutagenesis and computational modelling”, Nature Communications, 14(1).
Lu, X., Rainforth, T. and Teh, Y. (2023) “Daisee: Adaptive importance sampling by balancing exploration and exploitation”, Scandinavian Journal of Statistics, 50(3), pp. 1298–1324.
Lu, X., Rainforth, T. and Teh, Y. (2023) “Daisee: Adaptive importance sampling by balancing exploration and exploitation”, Scandinavian Journal of Statistics, 50(3), pp. 1298–1324.
Klarner, L., Rudner, T. and Morris, G. (2023) “Drug discovery under covariate shift with domain-informed prior distributions over functions”, in Krause, A. et al. (eds.). Journal of Machine Learning Research, pp. 17176–17197.