Travelled to:1 × Canada
1 × Finland
1 × France
1 × USA
2 × United Kingdom
Collaborated with:S.Shalev-Shwartz S.Chaudhuri H.G.Ramaswamy S.Agarwal O.Dekel R.Arora S.Shukla M.Lease S.M.Kakade S.Kalyanakrishnan P.Auer P.Stone C.Scherrer M.Halappanavar D.Haglin K.Chiang N.Natarajan I.S.Dhillon
Talks about:bandit (3) algorithm (2) stochast (2) regular (2) predict (2) regret (2) onlin (2) learn (2) list (2) multiclass (1)
Person: Ambuj Tewari
DBLP: Tewari:Ambuj
Contributed to:
20152012
2012
201120092008Wrote 9 papers:
- ICML-2015-RamaswamyT0 #classification
- Convex Calibrated Surrogates for Hierarchical Classification (HGR, AT, SA), pp. 1852–1860.
- ICML-2015-TewariC #bound #documentation #fault #learning #matter #question #rank
- Generalization error bounds for learning to rank: Does the length of document lists matter? (AT, SC), pp. 315–323.
- ICML-2012-DekelTA #adaptation #learning #online #policy
- Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret (OD, AT, RA), p. 227.
- ICML-2012-KalyanakrishnanTAS #multi #probability #set
- PAC Subset Selection in Stochastic Multi-armed Bandits (SK, AT, PA, PS), p. 34.
- ICML-2012-ScherrerHTH #algorithm #coordination #problem #scalability
- Scaling Up Coordinate Descent Algorithms for Large ℓ1 Regularization Problems (CS, MH, AT, DH), p. 50.
- SIGIR-2012-ShuklaLT #using
- Parallelizing ListNet training using spark (SS, ML, AT), pp. 1127–1128.
- CIKM-2011-ChiangNTD #network #predict
- Exploiting longer cycles for link prediction in signed networks (KYC, NN, AT, ISD), pp. 1157–1162.
- ICML-2009-Shalev-ShwartzT #probability
- Stochastic methods for l1 regularized loss minimization (SSS, AT), pp. 929–936.
- ICML-2008-KakadeST #algorithm #multi #online #performance #predict
- Efficient bandit algorithms for online multiclass prediction (SMK, SSS, AT), pp. 440–447.