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Open Knowledge
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Travelled to:
1 × Brazil
1 × Canada
1 × Germany
1 × Israel
1 × Italy
1 × United Kingdom
2 × China
7 × USA
Collaborated with:
C.Szepesvári D.Schuurmans C.Yu A.J.Grove S.(.Ravanbakhsh S.Wang T.V.Allen P.Orponen B.Póczos J.Wen A.Farhangfar C.Lee R.Isukapalli A.M.Elgammal T.Scheffer C.Darken A.Kogan D.Roth C.Srinivasa B.J.Frey L.Li X.Su T.M.Khoshgoftaar X.Zhu Alejandro Isaza Jieshan Lu Vadim Bulitko Y.Abbasi-Yadkori N.R.Sturtevant S.Wang L.Cheng
Talks about:
learn (10) model (4) classifi (3) approxim (3) use (3) distribut (2) strategi (2) exploit (2) select (2) effici (2)

Person: Russell Greiner

DBLP DBLP: Greiner:Russell

Contributed to:

ICML c2 20142014
ICML 20122012
ICML 20102010
ICML 20092009
SAC 20082008
ICML 20062006
ICPR v3 20062006
ICML 20052005
ICML 20002000
ICML 19971997
ICML 19961996
ICML 19951995
KR 19921992
PODS 19921992
KR 19911991
ML 19891989
AIIDE 20082008

Wrote 20 papers:

ICML-c2-2014-RavanbakhshSFG #graph #problem
Min-Max Problems on Factor Graphs (S(R, CS, BJF, RG), pp. 1035–1043.
ICML-c2-2014-WenYG #learning #nondeterminism #robust
Robust Learning under Uncertain Test Distributions: Relating Covariate Shift to Model Misspecification (JW, CNY, RG), pp. 631–639.
ICML-2012-RavanbakhshYG #approximate #modelling #visual notation
A Generalized Loop Correction Method for Approximate Inference in Graphical Models (S(R, CNY, RG), p. 84.
ICML-2010-LiPSG #learning #parametricity
Budgeted Distribution Learning of Belief Net Parameters (LL, BP, CS, RG), pp. 879–886.
ICML-2009-FarhangfarGS #image #learning
Learning to segment from a few well-selected training images (AF, RG, CS), pp. 305–312.
ICML-2009-PoczosASGS #exclamation #learning
Learning when to stop thinking and do something! (BP, YAY, CS, RG, NRS), pp. 825–832.
SAC-2008-SuKZG #classification #collaboration #machine learning #using
Imputation-boosted collaborative filtering using machine learning classifiers (XS, TMK, XZ, RG), pp. 949–950.
ICML-2006-LeeGW #classification #using
Using query-specific variance estimates to combine Bayesian classifiers (CHL, RG, SW), pp. 529–536.
ICPR-v3-2006-IsukapalliE #identification #learning #policy
Learning Policies for Efficiently Identifying Objects of Many Classes (RI, AME, RG), pp. 356–361.
ICML-2005-WangWGSC #markov #modelling #random #semantics
Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields (SW, SW, RG, DS, LC), pp. 948–955.
ICML-2000-AllenG #comparison #empirical #learning
Model Selection Criteria for Learning Belief Nets: An Empirical Comparison (TVA, RG), pp. 1047–1054.
ICML-1997-SchefferGD #why
Why Experimentation can be better than “Perfect Guidance” (TS, RG, CD), pp. 331–339.
Exploiting the Omission of Irrelevant Data (RG, AJG, AK), pp. 216–224.
ICML-1996-GreinerGR #classification #learning
Learning Active Classifiers (RG, AJG, DR), pp. 207–215.
ICML-1995-Greiner #challenge
The Challenge of Revising an Impure Theory (RG), pp. 269–277.
KR-1992-GreinerS #approximate #learning
Learning Useful Horn Approximations (RG, DS), pp. 383–392.
PODS-1992-Greiner #learning #performance #query
Learning Efficient Query Processing Strategies (RG), pp. 33–46.
KR-1991-GreinerO #approximate
Probably Approximately Optimal Derivation Strategies (RG, PO), pp. 277–288.
ML-1989-Greiner #analysis #formal method #towards
Towards a Formal Analysis of EBL (RG), pp. 450–453.
AIIDE-2008-IsazaLBG #approach #multi
A Cover-Based Approach to Multi-Agent Moving Target Pursuit (AI, JL, VB, RG).

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
Hosted as a part of SLEBOK on GitHub.