BibSLEIGH corpus
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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 DBLP: Schapire:Robert_E=

Contributed to:

ICML c1 20142014
ICML c2 20142014
ICML 20082008
ICML 20072007
ICML 20062006
ICML 20042004
ICML 20022002
CIKM 20002000
ICML 20002000
ICML 19981998
SIGIR 19981998
ICML 19971997
STOC 19971997
ICML 19961996
SIGIR 19961996
ICML 19941994
STOC 19941994
STOC 19931993
STOC 19891989

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.
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.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
Hosted as a part of SLEBOK on GitHub.