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Travelled to:
1 × China
1 × France
3 × USA
Collaborated with:
S.Agarwal P.Kar P.Jain S.Satheesh Pramala Senthil H.G.Ramaswamy A.Saha A.Agarwal S.Kalyanakrishnan A.K.Menon S.Chawla
Talks about:
class (4) optim (3) base (3) algorithm (2) perform (2) partial (2) consist (2) measur (2) binari (2) auc (2)

Person: Harikrishna Narasimhan

DBLP DBLP: Narasimhan:Harikrishna

Contributed to:

ICML 20152015
ICML c2 20142014
ICML c1 20132013
ICML c3 20132013
KDD 20132013
FDG 20092009

Wrote 8 papers:

ICML-2015-KarN0 #precise
Surrogate Functions for Maximizing Precision at the Top (PK, HN, PJ), pp. 189–198.
ICML-2015-NarasimhanK0 #metric #optimisation #performance
Optimizing Non-decomposable Performance Measures: A Tale of Two Classes (HN, PK, PJ), pp. 199–208.
ICML-2015-NarasimhanRS0 #algorithm #consistency #metric #multi #performance
Consistent Multiclass Algorithms for Complex Performance Measures (HN, HGR, AS, SA), pp. 2398–2407.
ICML-c2-2014-0001NKA #estimation #probability
GEV-Canonical Regression for Accurate Binary Class Probability Estimation when One Class is Rare (AA, HN, SK, SA), pp. 1989–1997.
ICML-c1-2013-NarasimhanA #approach #optimisation
A Structural SVM Based Approach for Optimizing Partial AUC (HN, SA), pp. 516–524.
ICML-c3-2013-MenonNAC #algorithm #classification #consistency #on the #statistics
On the Statistical Consistency of Algorithms for Binary Classification under Class Imbalance (AKM, HN, SA, SC), pp. 603–611.
KDD-2013-NarasimhanA #bound #named #optimisation
SVMpAUCtight: a new support vector method for optimizing partial AUC based on a tight convex upper bound (HN, SA), pp. 167–175.
FDG-2009-SatheeshNS #evolution #game studies
Evolving player-specific content for level based arcade games (SS, HN, PS), pp. 329–330.

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