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: Greiner:Russell
Contributed to:
2014201220102009
20082006
200620052000199719961995
1992
199219911989
2008Wrote 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.
- ICML-1996-GreinerGK
- 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).