Travelled to:1 × Canada
1 × China
1 × France
1 × Israel
1 × USA
1 × United Kingdom
Collaborated with:B.Schölkopf J.Peters E.Sgouritsa J.M.Mooij K.Zhang P.O.Hoyer N.Shajarisales M.Besserve S.Kpotufe A.Gretton X.Sun K.Fukumizu P.Geiger M.Gong D.W.Hogg D.Wang D.Foreman-Mackey C.Simon-Gabriel
Talks about:causal (6) infer (3) caus (3) effect (2) model (2) learn (2) addit (2) tell (2) nois (2) base (2)
Person: Dominik Janzing
DBLP: Janzing:Dominik
Contributed to:
201520142012201020092007Wrote 9 papers:
- ICML-2015-GeigerZSGJ #component #identification #process
- Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components (PG, KZ, BS, MG, DJ), pp. 1917–1925.
- ICML-2015-ScholkopfHWFJSP #fault
- Removing systematic errors for exoplanet search via latent causes (BS, DWH, DW, DFM, DJ, CJSG, JP), pp. 2218–2226.
- ICML-2015-ShajarisalesJSB #linear
- Telling cause from effect in deterministic linear dynamical systems (NS, DJ, BS, MB), pp. 285–294.
- ICML-c2-2014-KpotufeSJS #consistency
- Consistency of Causal Inference under the Additive Noise Model (SK, ES, DJ, BS), pp. 478–486.
- ICML-2012-ScholkopfJPSZM #learning #on the
- On causal and anticausal learning (BS, DJ, JP, ES, KZ, JMM), p. 63.
- ICML-2010-JanzingHS
- Telling cause from effect based on high-dimensional observations (DJ, POH, BS), pp. 479–486.
- ICML-2009-MooijJPS #dependence #modelling
- Regression by dependence minimization and its application to causal inference in additive noise models (JMM, DJ, JP, BS), pp. 745–752.
- ICML-2009-PetersJGS #detection
- Detecting the direction of causal time series (JP, DJ, AG, BS), pp. 801–808.
- ICML-2007-SunJSF #algorithm #kernel #learning
- A kernel-based causal learning algorithm (XS, DJ, BS, KF), pp. 855–862.