Travelled to:1 × Australia
1 × China
1 × France
1 × Israel
1 × United Kingdom
3 × USA
Collaborated with:E.Eban D.Sontag U.Heinemann X.Carreras S.T.Roweis N.Tishby S.Shalev-Shwartz E.Mezuman T.Roughgarden C.Yildirim A.Quattoni B.Balle O.Pele B.Taskar M.Werman A.Birnbaum O.Meshi T.S.Jaakkola T.Koo M.Collins R.Livni D.Lehavi S.Schein H.Nachlieli
Talks about:predict (3) linear (3) learn (3) structur (2) sequenc (2) analysi (2) effici (2) infer (2) exponenti (1) dimension (1)
Person: Amir Globerson
DBLP: Globerson:Amir
Contributed to:
20152014201320122010200720062002Wrote 11 papers:
- ICML-2015-GlobersonRSY #how #predict #question
- How Hard is Inference for Structured Prediction? (AG, TR, DS, CY), pp. 2181–2190.
- ICML-c2-2014-EbanMG #classification
- Discrete Chebyshev Classifiers (EE, EM, AG), pp. 1233–1241.
- ICML-c2-2014-HeinemannG #modelling #visual notation
- Inferning with High Girth Graphical Models (UH, AG), pp. 1260–1268.
- ICML-c2-2014-QuattoniBCG #sequence
- Spectral Regularization for Max-Margin Sequence Tagging (AQ, BB, XC, AG), pp. 1710–1718.
- ICML-c1-2013-LivniLSNSG #analysis #component
- Vanishing Component Analysis (RL, DL, SS, HN, SSS, AG), pp. 597–605.
- ICML-c1-2013-PeleTGW #classification #performance
- The Pairwise Piecewise-Linear Embedding for Efficient Non-Linear Classification (OP, BT, AG, MW), pp. 205–213.
- ICML-2012-EbanBSG #learning #online #predict #sequence
- Learning the Experts for Online Sequence Prediction (EE, AB, SSS, AG), p. 38.
- ICML-2010-MeshiSJG #approximate #learning
- Learning Efficiently with Approximate Inference via Dual Losses (OM, DS, TSJ, AG), pp. 783–790.
- ICML-2007-GlobersonKCC #algorithm #predict
- Exponentiated gradient algorithms for log-linear structured prediction (AG, TK, XC, MC), pp. 305–312.
- ICML-2006-GlobersonR #learning #robust
- Nightmare at test time: robust learning by feature deletion (AG, STR), pp. 353–360.
- ICML-2002-GlobersonT #analysis #novel #reduction
- Sufficient Dimensionality Reduction — A novel Analysis Method (AG, NT), pp. 203–210.