BibSLEIGH — Tobias

Travelled to:

1 × Finland
1 × Israel
1 × Slovenia
10 × USA
2 × Canada
2 × United Kingdom

Collaborated with:

U.Brefeld P.Haider S.Wrobel S.Bickel M.Brückner C.Sawade N.Landwehr S.Jaroszewicz K.Grabski ∅ T.Joachims U.Dick L.Dietz A.Zien D.S.Vogel O.Asparouhov R.Greiner C.Darken M.Großhans P.Prasse J.Bogojeska T.Lengauer T.Gärtner

Talks about:

learn (6) bayesian (3) regress (3) problem (3) predict (3) cluster (3) email (3) data (3) discoveri (2) adversari (2)

Person: Tobias Scheffer

DBLP: Scheffer:Tobias

Facilitated 1 volumes:

Contributed to:

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2001

2000

1999

1997

Wrote 23 papers:

ICML-c3-2013-GrosshansSBS #game studies #problem: Bayesian Games for Adversarial Regression Problems (MG, CS, MB, TS), pp. 55–63.
ICML-2012-HaiderS #clustering #graph #using: Finding Botnets Using Minimal Graph Clusterings (PH, TS), p. 37.
ICML-2012-PrasseSLS #email #identification #learning #regular expression: Learning to Identify Regular Expressions that Describe Email Campaigns (PP, CS, NL, TS), p. 146.
KDD-2011-BrucknerS #game studies #predict #problem: Stackelberg games for adversarial prediction problems (MB, TS), pp. 547–555.
ICML-2010-SawadeLBS #estimation: Active Risk Estimation (CS, NL, SB, TS), pp. 951–958.
ICML-2009-HaiderS #clustering #detection #email: Bayesian clustering for email campaign detection (PH, TS), pp. 385–392.
ICML-2008-BickelBLS #learning #multi: Multi-task learning for HIV therapy screening (SB, JB, TL, TS), pp. 56–63.
ICML-2008-DickHS #infinity #learning #semistructured data: Learning from incomplete data with infinite imputations (UD, PH, TS), pp. 232–239.
ICML-2007-BickelBS #learning: Discriminative learning for differing training and test distributions (SB, MB, TS), pp. 81–88.
ICML-2007-DietzBS #predict: Unsupervised prediction of citation influences (LD, SB, TS), pp. 233–240.
ICML-2007-HaiderBS #clustering #detection #email #streaming: Supervised clustering of streaming data for email batch detection (PH, UB, TS), pp. 345–352.
ICML-2007-ZienBS: Transductive support vector machines for structured variables (AZ, UB, TS), pp. 1183–1190.
KDD-2007-VogelAS #linear #scalability: Scalable look-ahead linear regression trees (DSV, OA, TS), pp. 757–764.
ICML-2006-BrefeldGSW #performance: Efficient co-regularised least squares regression (UB, TG, TS, SW), pp. 137–144.
ICML-2006-BrefeldS #learning: Semi-supervised learning for structured output variables (UB, TS), pp. 145–152.
KDD-2005-JaroszewiczS #network #performance: Fast discovery of unexpected patterns in data, relative to a Bayesian network (SJ, TS), pp. 118–127.
ICML-2004-BrefeldS #learning: Co-EM support vector learning (UB, TS).
SIGIR-2004-GrabskiS: Sentence completion (KG, TS), pp. 433–439.
ICML-2001-SchefferW #incremental #information management #problem: Incremental Maximization of Non-Instance-Averaging Utility Functions with Applications to Knowledge Discovery Problems (TS, SW), pp. 481–488.
ICML-2000-Scheffer #performance #predict: Predicting the Generalization Performance of Cross Validatory Model Selection Criteria (TS), pp. 831–838.
KDD-2000-SchefferW #algorithm: A sequential sampling algorithm for a general class of utility criteria (TS, SW), pp. 330–334.
ICML-1999-SchefferJ #analysis #fault: Expected Error Analysis for Model Selection (TS, TJ), pp. 361–370.
ICML-1997-SchefferGD #why: Why Experimentation can be better than “Perfect Guidance” (TS, RG, CD), pp. 331–339.