Travelled to:1 × Canada
1 × China
1 × Finland
1 × France
1 × Israel
1 × United Kingdom
2 × USA
Collaborated with:O.Dekel T.Zhang S.Shalev-Shwartz D.Kukliansky ∅ N.Srebro O.Avner S.Mannor A.Rakhlin K.Sridharan A.Gonen N.Cesa-Bianchi R.Gilad-Bachrach L.Xiao O.Tamuz C.Liu S.Belongie A.Kalai
Talks about:optim (6) distribut (3) stochast (3) effici (3) learn (3) gradient (2) attribut (2) descent (2) converg (2) convex (2)
Person: Ohad Shamir
DBLP: Shamir:Ohad
Contributed to:
20152014201320122011201020092008Wrote 12 papers:
- ICML-2015-KuklianskyS #linear #performance
- Attribute Efficient Linear Regression with Distribution-Dependent Sampling (DK, OS), pp. 153–161.
- ICML-2015-Shamir #algorithm #convergence #exponential #probability
- A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate (OS), pp. 144–152.
- ICML-c2-2014-ShamirS0 #approximate #distributed #optimisation #using
- Communication-Efficient Distributed Optimization using an Approximate Newton-type Method (OS, NS, TZ), pp. 1000–1008.
- ICML-c1-2013-Shamir0 #convergence #optimisation #probability
- Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes (OS, TZ), pp. 71–79.
- ICML-2012-AvnerMS #multi
- Decoupling Exploration and Exploitation in Multi-Armed Bandits (OA, SM, OS), p. 145.
- ICML-2012-RakhlinSS #optimisation #probability
- Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization (AR, OS, KS), p. 204.
- ICML-2011-DekelGSX #distributed #online #predict
- Optimal Distributed Online Prediction (OD, RGB, OS, LX), pp. 713–720.
- ICML-2011-Shalev-ShwartzGS #constraints #rank #scalability
- Large-Scale Convex Minimization with a Low-Rank Constraint (SSS, AG, OS), pp. 329–336.
- ICML-2011-TamuzLBSK #adaptation #kernel #learning
- Adaptively Learning the Crowd Kernel (OT, CL, SB, OS, AK), pp. 673–680.
- ICML-2010-Cesa-BianchiSS #learning #performance
- Efficient Learning with Partially Observed Attributes (NCB, SSS, OS), pp. 183–190.
- ICML-2009-DekelS #education
- Good learners for evil teachers (OD, OS), pp. 233–240.
- ICML-2008-DekelS #learning
- Learning to classify with missing and corrupted features (OD, OS), pp. 216–223.