Travelled to:1 × Austria
1 × Finland
1 × Sweden
1 × USA
1 × United Kingdom
2 × Canada
Collaborated with:C.Bauckhage C.Thurau K.Driessens R.Sifa Anders Drachen M.v.Otterlo L.D.Raedt Fabian Hadiji M.Wahabzada A.Pilz C.Plagemann P.Pfaff W.Burgard M.Neumann L.Hallau B.Klatt S.M.Kazemi D.Buchman S.Natarajan D.Poole Alessandro Canossa Julian Runge
Talks about:relat (3) play (3) influenc (2) regress (2) predict (2) player (2) game (2) base (2) heteroscedast (1) treatment (1)
Person: Kristian Kersting
DBLP: Kersting:Kristian
Contributed to:
2014
2014
20112010
200820072004
20122014
2015Wrote 10 papers:
- ICPR-2014-NeumannHKKB #classification #image
- Erosion Band Features for Cell Phone Image Based Plant Disease Classification (MN, LH, BK, KK, CB), pp. 3315–3320.
- KR-2014-KazemiBKNP #relational
- Relational Logistic Regression (SMK, DB, KK, SN, DP).
- CIKM-2011-WahabzadaKPB #performance #scheduling
- More influence means less work: fast latent dirichlet allocation by influence scheduling (MW, KK, AP, CB), pp. 2273–2276.
- CIKM-2010-ThurauKB #matrix
- Yes we can: simplex volume maximization for descriptive web-scale matrix factorization (CT, KK, CB), pp. 1785–1788.
- ICML-2008-KerstingD #parametricity #policy #relational
- Non-parametric policy gradients: a unified treatment of propositional and relational domains (KK, KD), pp. 456–463.
- ICML-2007-KerstingPPB #process
- Most likely heteroscedastic Gaussian process regression (KK, CP, PP, WB), pp. 393–400.
- ICML-2004-KerstingOR #relational
- Bellman goes relational (KK, MvO, LDR).
- CIG-2012-BauckhageKSTDC #empirical #game studies #how
- How players lose interest in playing a game: An empirical study based on distributions of total playing times (CB, KK, RS, CT, AD, AC), pp. 139–146.
- CIG-2014-HadijiSDTKB #predict
- Predicting player churn in the wild (FH, RS, AD, CT, KK, CB), pp. 1–8.
- AIIDE-2015-SifaHRDKB #game studies #mobile #predict
- Predicting Purchase Decisions in Mobile Free-to-Play Games (RS, FH, JR, AD, KK, CB), pp. 79–85.