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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
About me
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Posts
Future Blog Post
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Blog Post number 4
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
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publications
Dimension Formulae for Iterated Function Systems
Unpublished MSci dissertation. The majority of the dissertation is an exposition of prior books and papers, but the final 10 pages contain original material, culminating in Theorem 7.19 which gives a lower bound on the Hausdorff dimension of a certain class of planar fractals.
Download here
Variational Noise-Contrastive Estimation
Published in The 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 2019
We propose a new method for estimating the parameters of energy-based, latent variable models. The core contribution is the derivation of a variational lower bound for the noise-contrastive estimation objective function.
Recommended citation: Rhodes, B. and Gutmann, M. U. (2019). Variational noise-contrastive estimation. InThe 22nd InternationalConference on Artificial Intelligence and Statistics, pages 2741–2750. http://benrhodes.github.io/files/vnce.pdf
Telescoping Density-Ratio Estimation
Published in Advances in Neural Information Processing Systems - Spotlight (top 4% of submissions), 2020
We propose a new framework, Telescoping Density-ratio Estimation (TRE), that enables the estimation of ratios between highly dissimilar densities in high-dimensional spaces.
Recommended citation: Rhodes, B., Xu, K., and Gutmann, M. (2020). Telescoping Density-Ratio Estimation. In Advances in Neural Information Processing Systems http://benrhodes26.github.io/files/tre.pdf
Enhanced gradient-based MCMC in discrete spaces
Published in Transactions on Machine Learning Research, 2022
We introduce several discrete Metropolis-Hastings samplers that are conceptually inspired by MALA, and demonstrate their strong empirical performance across a range of challenging sampling problems in Bayesian inference and energy-based modelling. Methodologically, we identify why discrete analogues to \emph{preconditioned} MALA are generally intractable, motivating us to introduce a new kind of preconditioning based on auxiliary variables and the “Gaussian integral trick”.
Recommended citation: Enhanced gradient-based MCMC in discrete spaces. Rhodes, B. and Gutmann, M. Transactions on Machine Learning Research (2022). http://benrhodes26.github.io/files/enhanced_mcmc.pdf
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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