James Lloyd punting on the Cam

James Robert Lloyd

About Me

I am studying for a PhD in Machine Learning at the University of Cambridge. My supervisor is Zoubin Ghahramani and my advisor is Carl Edward Rasmussen.

I joined the machine learning group at Cambridge in 2011 after working as a management consultant at the Boston Consulting Group. Before that, I received a B.A. in mathematics and M.Phil in Statistics from the University of Cambridge.

My interests lie in the application of Bayesian and nonparametric statistics to machine learning. In particular, I am working towards building an automatic statistician.

Curriculum vitae.


Publications

Automatic Construction and Natural-Language Description of Nonparametric Regression Models
James Robert Lloyd, David Duvenaud, Roger Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani
Association for the Advancement of Artificial Intelligence (AAAI) Conference, 2014
preprint | slides | code | example report - airline | example report - solar | more examples | bibtex
Gaussian Process Conditional Copulas with Applications to Financial Time Series
José Miguel Hernández-Lobato, James Robert Lloyd, Daniel Hernández-Lobato
Neural Information Processing Systems, 2013
pdf | code | bibtex
GEFCom2012 Hierarchical Load Forecasting: Gradient Boosting Machines and Gaussian Processes
James Robert Lloyd
International Journal of Forecasting, 2013
Presented at IEEE Power and Energy Society General Meeting 2013
preprint | published | code | slides | bibtex
Structure Discovery in Nonparametric Regression through Compositional Kernel Search
David Duvenaud, James Robert Lloyd, Roger Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani
International Conference on Machine Learning, 2013
pdf | code | poster | bibtex
Random function priors for exchangeable arrays with applications to graphs and relational data
James Robert Lloyd, Peter Orbanz, Zoubin Ghahramani, Daniel Roy
Neural Information Processing Systems, 2012
pdf | code | poster | bibtex

Talks

Building an automatic statistician
Microsoft Research Cambridge
slides | video
Introduction to Probabilistic Programming and Automated Inference
Computational and Biological Learning Lab, University of Cambridge, March 2013
With David Duvenaud
slides
Random function priors for exchangeable databases
9th Conference on Bayesian Nonparametrics
ERCIM 2013
slides
Bayesian nonparametric network models: latent space and latent attribute approaches
Netsci 2013: Complex Networks meet Machine Learning
slides


Contact

jrl44@cam.ac.uk

Cambridge University Engineering Department
Trumpington Street
Cambridge
CB2 1PZ
United Kingdom

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