Travelled to:1 × China
1 × Finland
1 × France
1 × Germany
1 × United Kingdom
5 × USA
Collaborated with:S.Shalev-Shwartz A.Cotter B.Neyshabur A.Jalali J.D.M.Rennie T.S.Jaakkola O.Shamir T.Zhang J.Wang J.Evans J.Keshet X.Zhou M.Belkin Y.Singer G.Shakhnarovich S.T.Roweis S.Ben-David D.Loker K.Sridharan Y.Amit M.Fink S.Ullman
Talks about:optim (3) use (3) collabor (2) approxim (2) approach (2) cluster (2) kernel (2) train (2) rank (2) svm (2)
Person: Nathan Srebro
DBLP: Srebro:Nathan
Contributed to:
20152014
20142012201120082007200620052003Wrote 14 papers:
- ICML-2015-NeyshaburS #on the #symmetry
- On Symmetric and Asymmetric LSHs for Inner Product Search (BN, NS), pp. 1926–1934.
- ICML-c2-2014-ShamirS0 #approximate #distributed #optimisation #using
- Communication-Efficient Distributed Optimization using an Approximate Newton-type Method (OS, NS, TZ), pp. 1000–1008.
- KDD-2014-WangSE #collaboration #learning #permutation
- Active collaborative permutation learning (JW, NS, JE), pp. 502–511.
- ICML-2012-Ben-DavidLSS #classification #fault #using
- Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss (SBD, DL, NS, KS), p. 16.
- ICML-2012-CotterSS #kernel #probability
- The Kernelized Stochastic Batch Perceptron (AC, SSS, NS), p. 98.
- ICML-2012-JalaliS12a #clustering #optimisation #using
- Clustering using Max-norm Constrained Optimization (AJ, NS), p. 205.
- KDD-2011-CotterSK #approach #kernel
- A GPU-tailored approach for training kernelized SVMs (AC, NS, JK), pp. 805–813.
- KDD-2011-ZhouBS #approach #graph #ranking
- An iterated graph laplacian approach for ranking on manifolds (XZ, MB, NS), pp. 877–885.
- ICML-2008-Shalev-ShwartzS #dependence #optimisation #set
- SVM optimization: inverse dependence on training set size (SSS, NS), pp. 928–935.
- ICML-2007-AmitFSU #classification #multi
- Uncovering shared structures in multiclass classification (YA, MF, NS, SU), pp. 17–24.
- ICML-2007-Shalev-ShwartzSS #named
- Pegasos: Primal Estimated sub-GrAdient SOlver for SVM (SSS, YS, NS), pp. 807–814.
- ICML-2006-SrebroSR #clustering
- An investigation of computational and informational limits in Gaussian mixture clustering (NS, GS, STR), pp. 865–872.
- ICML-2005-RennieS #collaboration #matrix #performance #predict
- Fast maximum margin matrix factorization for collaborative prediction (JDMR, NS), pp. 713–719.
- ICML-2003-SrebroJ #approximate #rank
- Weighted Low-Rank Approximations (NS, TSJ), pp. 720–727.