Staff profile

• Bayesian Statistics
• Computational Statistics
• Deep probabilistic and deep generative models
• Markov Chain Monte Carlo
• Inference for Complex Simulation Models
• Healthcare and Precision Medicine

Conference Paper

• Wyatt, Julian, Leach, Adam, Schmon, Sebastian M. & Willcocks, Chris G. (2022), AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. New Orleans, LA, IEEE, 650-656.
• Dyer, Joel, Cannon, Patrick & Schmon, Sebastian M. (2022), Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation, in Camps-Valls, Gustau, Ruiz, Francisco J.R. & Valera, Isabel eds, Proceedings of Machine Learning Research 151: Artificial Intelligence and Statistics 2022 (AISTATS): The 25th International Conference on Artificial Intelligence and Statistics. Virtual, AISTATS.
• Joy, Tom, Shi, Yuge, Torr, Philip H.S., Rainforth, Tom, Schmon, Sebastian M. & Siddharth, N. (2022), Learning Multimodal VAEs Through Mutual Supervision, ICLR 2022: The Tenth International Conference on Learning Representations. Virtual, ICLR.
• Leach, Adam, Schmon, Sebastian M., Degiacomi, Matteo T. & Willcocks, Chris G. (2022), Denoising Diffusion Probabilistic Models on SO(3) for Rotational Alignment, ICLR 2022 Workshop on Geometrical and Topological Representation Learning. ICLR.
• Tom Joy, Sebastian Schmon, Philip Torr, Siddharth N & Tom Rainforth (2021), Capturing Label Characteristics in \VAE\s, International Conference on Learning Representations.

Journal Article

• Schmon, Sebastian M. & Gagnon, Philippe (2022). Optimal scaling of random-walk Metropolis algorithms using Bayesian large-sample asymptotics. Statistics and Computing 32(2): 28.