I am a research associate working with Arthur Gretton
at the Gatsby Computational Neuroscience Unit.
My main research interests are information theory (consistent estimators, distribution regression; ITE toolbox
), kernel methods and dictionary learning problems (structured sparse tasks, independent subspace analysis
and its extensions). I am also working on applications including remote sensing, blind signal separation, collaborative filtering,
natural language processing and facial expression recognition.
- Two-stage Sampled Learning Theory on Distributions: accepted for oral presentation at AISTATS-2015. [6.11% oral acceptance rate, paper; arXiv: abstract, paper; code; Jan. 11, 2015]
- Consistent Vector-valued Distribution Regression. [presented at UCL Workshop on the Theory of Big Data; abstract, slides, code; Jan. 8, 2015]
- TR: Learning Theory for Distribution Regression. [arXiv: abstract, paper; significant extension of abstract, paper; code; Dec. 6, 2014]
- TR: Bayesian Manifold Learning: Locally Linear Latent Variable Model (LL-LVM). [arXiv: abstract, paper; Oct. 27, 2014]
- Invited talk:
- Centre for Research in Statistical Methodology (CRiSM), Department of Statistics, University of Warwick. [May. 29, 2015]
- Department of Statistics, Oxford. [May. 1, 2015]
- A Simple and Consistent Technique for Vector-valued Distribution Regression. [Artificial Intelligence and Natural Computation seminars, University of Birmingham (talk details); abstract, slides, code; Jan. 26, 2015]
- Consistent Vector-valued Regression on Probability Measures. [Prof. Bernhard Schölkopf's lab in Tübingen; abstract, slides, code; Jan. 14-18, 2015]