Caroline Uhler (MIT), gives a OxCSML Seminar on Friday 2nd July 2021. Abstract: Massive data collection holds the promise of a better understanding of...
Recent Applications of Stein's Method in Machine Learning
Qiang Liu (University of Texas at Austin) gives the OxCSML Seminar on Friday 4th June 2021. Abstract: Stein's method is a powerful technique for deriv...
Do Simpler Models Exist and How Can We Find Them?
Cynthia Rudin (Duke University) gives a OxCSML Seminar on Friday 14th May 2021. Abstract: While the trend in machine learning has tended towards more ...
Practical pre-asymptotic diagnostic of Monte Carlo estimates in Bayesian inference and machine learning
Aki Vehtari (Aalto University) gives the OxCSML Seminar on Friday 7th May 2021 Abstract: I discuss the use of the Pareto-k diagnostic as a simple and ...
Complexity of local MCMC methods for high-dimensional model selection
Quan Zhou, Texas A and M University, gives an OxCSML Seminar on Friday 25th June 2021. Abstract: In a model selection problem, the size of the state s...
Assessing Personalization in Digital Health
Distinguished Speaker Seminar - Friday 18th June 2021, with Susan Murphy, Professor of Statistics and Computer Science, Harvard John A. Paulson School...
Machine Learning in Drug Discovery
Graduate Lecture - Thursday 3rd June 2021, with Dr Fergus Boyles. Department of Statistics, University of Oxford. Drug discovery is a long and laborio...
Several structured thresholding bandit problems
OxCSML Seminar - Friday 28th May 2021, presented by Alexandra Carpentier (University of Magdeburg). In this talk we will discuss the thresholding band...
A primer on PAC-Bayesian learning *followed by* News from the PAC-Bayes frontline
Benjamin Guedj, University College London, gives a OxCSML Seminar on 26th March 2021. Abstract: PAC-Bayes is a generic and flexible framework to addre...
Approximate Bayesian computation with surrogate posteriors
Julyan Arbel (Inria Grenoble - Rhône-Alpes), gives an OxCSML Seminar on Friday 30th April 2021, for the Department of Statistics.