Travelled to:1 × Australia
1 × China
1 × Finland
1 × France
1 × Germany
1 × Israel
1 × Taiwan
4 × Canada
6 × USA
Collaborated with:M.Mohri D.Pregibon A.Rastogi A.Rostamizadeh C.Allauzen V.Kuznetsov U.Syed L.D.Jackel ∅ H.Drucker V.Vapnik A.M.Medina J.Weston W.Chiang D.Pechyony K.Fisher A.Rogers D.Hoover W.DuMouchel C.Volinsky T.Johnson Y.LeCun
Talks about:kernel (7) algorithm (6) learn (6) automata (3) general (3) probabilist (2) transduct (2) structur (2) regress (2) predict (2)
Person: Corinna Cortes
DBLP: Cortes:Corinna
Contributed to:
2015
201520142013
201020112010200920082007200620072005200420001999199819951994Wrote 23 papers:
- ICML-2015-CortesKMS #modelling
- Structural Maxent Models (CC, VK, MM, US), pp. 391–399.
- KDD-2015-CortesMM #adaptation #algorithm
- Adaptation Algorithm and Theory Based on Generalized Discrepancy (CC, MM, AMM), pp. 169–178.
- ICML-c2-2014-CortesKM #predict
- Ensemble Methods for Structured Prediction (CC, VK, MM), pp. 1134–1142.
- ICML-c2-2014-CortesMS
- Deep Boosting (CC, MM, US), pp. 1179–1187.
- ICML-c3-2013-CortesMR #classification #kernel #multi
- Multi-Class Classification with Maximum Margin Multiple Kernel (CC, MM, AR), pp. 46–54.
- CIAA-2010-AllauzenCM #automaton #kernel #scalability
- Large-Scale Training of SVMs with Automata Kernels (CA, CC, MM), pp. 17–27.
- CIAA-J-2010-AllauzenCM11 #algorithm #coordination #kernel
- A Dual Coordinate Descent Algorithm for SVMs Combined with Rational Kernels (CA, CC, MM), pp. 1761–1779.
- ICML-2010-CortesMR #algorithm #kernel #learning
- Two-Stage Learning Kernel Algorithms (CC, MM, AR), pp. 239–246.
- ICML-2010-CortesMR10a #bound #kernel #learning
- Generalization Bounds for Learning Kernels (CC, MM, AR), pp. 247–254.
- ICML-2009-Cortes #kernel #learning #performance #question
- Invited talk: Can learning kernels help performance? (CC), p. 1.
- ICML-2008-CortesMPR #algorithm
- Stability of transductive regression algorithms (CC, MM, DP, AR), pp. 176–183.
- ICML-2007-CortesMR #algorithm #ranking
- Magnitude-preserving ranking algorithms (CC, MM, AR), pp. 169–176.
- CIAA-2006-CortesMR #automaton #on the #probability #standard
- On the Computation of Some Standard Distances Between Probabilistic Automata (CC, MM, AR), pp. 137–149.
- CIAA-J-2006-CortesMR07 #automaton #distance #equivalence #probability
- LP Distance and Equivalence of Probabilistic Automata (CC, MM, AR), pp. 761–779.
- ICML-2005-CortesMW #learning
- A general regression technique for learning transductions (CC, MM, JW), pp. 153–160.
- ICML-2004-CortesM #kernel
- Distribution kernels based on moments of counts (CC, MM).
- KDD-2000-CortesFPR #data type #named
- Hancock: a language for extracting signatures from data streams (CC, KF, DP, AR), pp. 9–17.
- KDD-1999-CortesP #agile #deployment #framework #mining #platform
- Information Mining Platforms: An Infrastructure for KDD Rapid Deployment (CC, DP), pp. 327–331.
- KDD-1999-DuMouchelVJCP
- Squashing Flat Files Flatter (WD, CV, TJ, CC, DP), pp. 6–15.
- KDD-1998-CortesP
- Giga-Mining (CC, DP), pp. 174–178.
- KDD-1995-CortesDHV #capacity #complexity #predict
- Capacity and Complexity Control in Predicting the Spread Between Borrowing and Lending Interest Rates (CC, HD, DH, VV), pp. 51–56.
- KDD-1995-CortesJC #learning #quality
- Limits on Learning Machine Accuracy Imposed by Data Quality (CC, LDJ, WPC), pp. 57–62.
- ICML-1994-DruckerCJCV #algorithm #machine learning
- Boosting and Other Machine Learning Algorithms (HD, CC, LDJ, YL, VV), pp. 53–61.