We meet every week on Friday at 14:00. If you are interested in giving a talk, email Connor (email@example.com) to organise a date and time.
2022-01-21 - TBA - Toni Karvonen
Michaelmas Term 2021
2021-12-10 - Bayesian Learning via Neural Schrodinger Follmer Flow - Francisco Vargas Palomo
2021-11-26 - Kronecker-based network generation and analysis - Thomas Gaskin
2021-11-19 - Satellite remote sensing (Part II) - Andrea Marinoni
2021-11-12 - Learning based multiscale modeling - Burigede Liu
2021-11-05 - Learning from Comparisons - Stratis Ioannidis
2021-10-29 - Ordinal Patterns for Nonlinear Time Series Analysis - Thomas Stemler (starts at 10:00)
2021-10-22 - Satellite remote sensing (Part I) - Sivasakthy Selvakumaran (starts at 15:00)
2021-10-15 - No talk this week
2021-10-08 - Information Transfer for Engineering Fleets: Multi-Task Learning with Hierarchical Bayes - Lawrence Bull
2021-10-01 - Climate inference on daily rainfall across the Australian continent, 1876–2015 - Edward Cripps
2021-09-24 - No reading group this week
2021-09-17 - Statistical Finite Elements via Langevin Dynamics - Omer Deniz Akyildiz
2021-09-10 - Risk Based Structural Integrity Management - Domenic Di Francesco
Jan will present the Stochastic Variational Gradient Descent paper.
The main reference is:
Liu, Q. and D. Wang (2016). Stein variational Gradient descent: a general purpose Bayesian inference algorithm. Proceedings of the 30th International Conference on Neural Information Processing Systems. Barcelona, Spain, Curran Associates Inc.: 2378–2386. arXiv link
Alessandro will present the following three papers:
Barp, Alessandro, Francois-Xavier Briol, Andrew Duncan, Mark Girolami, and Lester Mackey. “Minimum Stein Discrepancy Estimators.” Paper presented at the Advances in Neural Information Processing Systems, 2019. NeurIPS link
Dawid, Alexander Philip, and Monica Musio. “Theory and Applications of Proper Scoring Rules.” METRON 72, no. 2 (2014): 169-83. DOI link. research gate link.
Hyvärinen, Aapo. “Estimation of Non-Normalized Statistical Models by Score Matching.” Journal of Machine Learning Research 6, no. Apr (2005): 695-709. JMLR link
2020-11-27 Introduction to Kernel Methods II with the focus on MMD
We will discuss the second part of the MLSS 2020 lecture on Kernel Methods by Arthur Gretton, with the focus on MMD.
2020-11-20 Introduction to Kernel Methods I (notes)
Justin will present Arthur Gretton’s introductory lecture into kernel methods.
2020-11-13 Sequential Monte Carlo without Likelihoods (notes)
Andrius will present the following paper:
Sisson, S. A., Y. Fan, and M. M. Tanaka. “Sequential Monte Carlo without Likelihoods.” Proceedings of the National Academy of Sciences 104, no. 6 (February 6, 2007): 1760–65. https://doi.org/10.1073/pnas.0607208104.
2020-11-06 ABC Methods for Climate Modelling (notes)