BibSLEIGH — Robert_E

Travelled to:

1 × Finland
1 × Italy
1 × Switzerland
10 × USA
2 × Australia
2 × Canada
2 × China

Collaborated with:

Y.Singer Y.Freund M.K.Warmuth M.Dudík A.Singhal H.Luo L.Reyzin ∅ R.L.Rivest R.D.Iyer D.D.Lewis S.Kale D.P.Helmbold U.Syed M.H.Bowling D.M.Blei S.J.Phillips E.L.Allwein M.J.Kearns D.Ron R.Rubinfeld L.Sellie A.Agarwal E.Hazan M.Rochery M.G.Rahim N.K.Gupta P.Barlett W.S.Lee J.P.Callan R.Papka P.Stone D.A.McAllester M.L.Littman J.A.Csirik A.Agarwal D.Hsu J.Langford L.Li Y.Mansour N.Cesa-Bianchi D.Haussler

Talks about:

boost (10) use (7) algorithm (6) learn (5) classifi (3) margin (3) multiclass (2) portfolio (2) distribut (2) automata (2)

Person: Robert E. Schapire

DBLP: Schapire:Robert_E=

Contributed to:

2014

2008

2007

2006

2004

2002

2000

1998

1997

1996

1994

1993

1989

Wrote 24 papers:

ICML-c1-2014-LuoS #learning #online #towards: Towards Minimax Online Learning with Unknown Time Horizon (HL, RES), pp. 226–234.
ICML-c2-2014-AgarwalHKLLS #algorithm #performance: Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits (AA, DH, SK, JL, LL, RES), pp. 1638–1646.
ICML-2008-SyedBS #learning #linear #programming #using: Apprenticeship learning using linear programming (US, MHB, RES), pp. 1032–1039.
ICML-2007-DudikBS #estimation: Hierarchical maximum entropy density estimation (MD, DMB, RES), pp. 249–256.
ICML-2006-AgarwalHKS #algorithm: Algorithms for portfolio management based on the Newton method (AA, EH, SK, RES), pp. 9–16.
ICML-2006-ReyzinS #classification #complexity #how: How boosting the margin can also boost classifier complexity (LR, RES), pp. 753–760.
ICML-2004-PhillipsDS #approach #modelling: A maximum entropy approach to species distribution modeling (SJP, MD, RES).
ICML-2002-SchapireRRG #information management: Incorporating Prior Knowledge into Boosting (RES, MR, MGR, NKG), pp. 538–545.
ICML-2002-SchapireSMLC #estimation #modelling #nondeterminism #using: Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation (RES, PS, DAM, MLL, JAC), pp. 546–553.
CIKM-2000-IyerLSSS #documentation: Boosting for Document Routing (RDI, DDL, RES, YS, AS), pp. 70–77.
ICML-2000-AllweinSS #approach #classification #multi: Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers (ELA, RES, YS), pp. 9–16.
ICML-1998-FreundISS #algorithm #performance: An Efficient Boosting Algorithm for Combining Preferences (YF, RDI, RES, YS), pp. 170–178.
SIGIR-1998-SchapireSS: Boosting and Rocchio Applied to Text Filtering (RES, YS, AS), pp. 215–223.
ICML-1997-Schapire #learning #multi #problem #using: Using output codes to boost multiclass learning problems (RES), pp. 313–321.
ICML-1997-SchapireFBL #effectiveness: Boosting the margin: A new explanation for the effectiveness of voting methods (RES, YF, PB, WSL), pp. 322–330.
STOC-1997-FreundSSW #predict #using: Using and Combining Predictors That Specialize (YF, RES, YS, MKW), pp. 334–343.
ICML-1996-FreundS #algorithm: Experiments with a New Boosting Algorithm (YF, RES), pp. 148–156.
ICML-1996-HelmboldSSW #multi #online #using: On-Line Portfolio Selection Using Multiplicative Updates (DPH, RES, YS, MKW), pp. 243–251.
SIGIR-1996-LewisSCP #algorithm #classification #linear: Training Algorithms for Linear Text Classifiers (DDL, RES, JPC, RP), pp. 298–306.
ICML-1994-SchapireW #algorithm #analysis #learning #on the #worst-case: On the Worst-Case Analysis of Temporal-Difference Learning Algorithms (RES, MKW), pp. 266–274.
STOC-1994-KearnsMRRSS #on the: On the learnability of discrete distributions (MJK, YM, DR, RR, RES, LS), pp. 273–282.
STOC-1993-Cesa-BianchiFHHSW #how: How to use expert advice (NCB, YF, DPH, DH, RES, MKW), pp. 382–391.
STOC-1993-FreundKRRSS #automaton #finite #learning #performance #random: Efficient learning of typical finite automata from random walks (YF, MJK, DR, RR, RES, LS), pp. 315–324.
STOC-1989-RivestS #automaton #finite #sequence #using: Inference of Finite Automata Using Homing Sequences (RLR, RES), pp. 411–420.