Doris Entner
Contact Information
Department of Computer Science P.O. box 68 (Gustaf Hällströmin katu 2b)
FI-00014 University of Helsinki
Finland
E-Mail: firstname.lastname@helsinki.fi
Phone: +358 9 191 51258
Office: Room A348 (Exactum)
Research
I am a doctoral student, supervised by Patrik Hoyer, at the
Helsinki Institute for Information Technology (HIIT)
in the Neuroinformatics research group. My research interests include graphical models, causal discovery, and time series.
Publications
A. Moneta, D. Entner, P.O. Hoyer, and A. Coad (Submitted)Causal inference by Independent Component Analysis with applications to micro- and macro-economic data [code package]
Available as Jena Economic Research Papers, 2010-031 [link]
(Introduces a method for SVAR identification using ICA to the econometrics community and shows how to apply it to economic data.)
D. Entner, and P.O. Hoyer (accepted for publication in AISTATS 2012)
Statistical test for consistent estimation of causal effects in linear non-Gaussian models
(Introduces a statistical test for linear non-Gaussian acyclic models to infer merely from observational data whether an estimator is consistent.)
D. Entner, and P.O. Hoyer (2011)
Discovering Unconfounded Causal Relationships Using Linear Non-Gaussian Models [pdf] [code package]
New Frontiers in Artificial Intelligence, LNAI 6797
(Presents an algorithm for Linear Non-Gaussian Acyclic Models with hidden variables to discover pairwise unconfounded total effects.)
(The original publication is available at www.springerlink.com.)
A. Moneta, N. Chlass, D. Entner, and P.O. Hoyer (2011)
Causal Search in Structural Vector Autoregressive Models. [pdf]
JMLR Workshop and Conference Proceedings 12:95-118
(Reviews a class of methods for causal inference in SVAR models.)
D. Entner, and P.O. Hoyer (2010)
On causal discovery from time series data using FCI [pdf] [code package]
Proceedings of the 5th European Workshop on Probabilistic Graphical Models (PGM), Helsinki, Finland
(Adapts the FCI algorithm of Spirtes et al. (2000) to learn causal relationships from time series data in the presence of confounding variables.)
Courses
Autumn 2011 (second period) Teaching assistent, Introduction to Machine Learning
Autumn 2010 (second period)
Teaching assistent, Introduction to Machine Learning
Spring 2010 (fourth period)
Teaching assistent, Unsupervised Machine Learning