I am a second year PhD student in the Machine Learning Group. My supervisor is Carl Edward Rasmussen, and my advisor is Zoubin Ghahramani. I am interested in the mathematics and statistical theory underlying machine learning algorithms, in particular Bayesian nonparametrics and reproducing kernel Hilbert spaces. Before starting my PhD I also worked on computational cognitive science in Máté Lengyel’s Computational Learning and Memory Group. More recently I have gained interest in quantum information and statistics.
For a graphical representation of my research interests click here.
|Huszár F and Houlsby NMT.Adaptive Bayesian Quantum Tomographysubmitted for review[ arXiv ]|
Huszár F and Lacoste-Julien S.
A kernel approach to tractable Bayesian Nonparametrics.
[ arXiv ]
Houlsby NMT, Huszár F, Ghahramani Z, Lengyel M.
Bayesian active learning by disagreement (BALD).
Peer-reviewed conference papers
Lacoste-Julien S, Huszár F, Ghahramani Z.
Approximate inference for the loss-calibrated Bayesian.
Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, JMLR W&CP 15:416-424, 2011
Huszár F, Noppeney U, Lengyel M.
Mind reading by machine learning: A doubly Bayesian method for inferring mental representations.
Proceedings of the 32nd Annual Conference of the Cognitive Science Society, 2810-2815, 2010.
Szathmáry E, Szatmáry Z, Ittzes P, Számadó S, Zachar I, Huszár F, Fedor A, Varga M.
In silico evolutionary developmental neurobiology and the origin of natural language.
In (C. Lyon, C. et al. eds) Emergence of Communication and Language, 151-188. 2007.
Technical reports, etc
Huszár F, O’Keeffe SG, Hein J.
Corner cutting approaches to Ethier-Griffiths-Tavaré recursions.
Technical report, Genome Analysis and Bioinformatics Group, Department of Statistics, University of Oxford, 2008.