BibSLEIGH — Rich

Travelled to:

1 × Australia
1 × Finland
1 × Germany
1 × Greece
1 × Italy
13 × USA
2 × Canada
2 × China

Collaborated with:

A.Niculescu-Mizil ∅ J.D.Schaffer Y.Lou J.Gehrke D.Freitag N.Nguyen S.Baluja D.Sorokina M.Riedewald D.Fink Y.Ganjisaffar C.V.Lopes N.Karampatziakis A.Yessenalina C.Bucila L.J.Eshelman G.Hooker G.Crew A.Ksikes J.O'Sullivan J.Langford A.Blum T.Kulesza S.Amershi D.Fisher D.X.Charles E.Ipek S.A.McKee B.R.d.Supinski M.Schulz A.L.Berger D.Cohn V.O.Mittal P.Koch M.Sturm N.Elhadad M.F.Elhawary A.Munson W.M.Hochachka S.Kelling

Talks about:

model (8) learn (8) algorithm (6) supervis (4) predict (4) genet (4) intellig (3) empir (3) bias (3) represent (2)

Person: Rich Caruana

DBLP: Caruana:Rich

Facilitated 1 volumes:

Contributed to:

2015

2014

2013

2012

2011

2008

2006

2005

2004

2000

1996

1995

1994

1993

1989

1988

Wrote 24 papers:

KDD-2015-CaruanaLGKSE #modelling #predict: Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission (RC, YL, JG, PK, MS, NE), pp. 1721–1730.
CHI-2014-KuleszaACFC #concept #evolution #machine learning: Structured labeling for facilitating concept evolution in machine learning (TK, SA, RC, DF, DXC), pp. 3075–3084.
CIKM-2013-Caruana #approximate #clustering #named #question: Clustering: probably approximately useless? (RC), pp. 1259–1260.
KDD-2013-LouCGH #interactive #modelling: Accurate intelligible models with pairwise interactions (YL, RC, JG, GH), pp. 623–631.
KDD-2012-LouCG #classification #modelling: Intelligible models for classification and regression (YL, RC, JG), pp. 150–158.
SIGIR-2011-GanjisaffarCL #modelling #precise #ranking: Bagging gradient-boosted trees for high precision, low variance ranking models (YG, RC, CVL), pp. 85–94.
ICML-2008-CaruanaKY #empirical #evaluation #learning: An empirical evaluation of supervised learning in high dimensions (RC, NK, AY), pp. 96–103.
ICML-2008-SorokinaCRF #detection #interactive #statistics: Detecting statistical interactions with additive groves of trees (DS, RC, MR, DF), pp. 1000–1007.
KDD-2008-NguyenC #classification: Classification with partial labels (NN, RC), pp. 551–559.
ASPLOS-2006-IpekMCSS #architecture #design #modelling #predict: Efficiently exploring architectural design spaces via predictive modeling (EI, SAM, RC, BRdS, MS), pp. 195–206.
ICML-2006-CaruanaN #algorithm #comparison #empirical #learning: An empirical comparison of supervised learning algorithms (RC, ANM), pp. 161–168.
KDD-2006-BucilaCN: Model compression (CB, RC, ANM), pp. 535–541.
KDD-2006-CaruanaEMRSFHK #mining #predict: Mining citizen science data to predict orevalence of wild bird species (RC, MFE, AM, MR, DS, DF, WMH, SK), pp. 909–915.
ICML-2005-Niculescu-MizilC #learning #predict: Predicting good probabilities with supervised learning (ANM, RC), pp. 625–632.
ICML-2004-CaruanaNCK #library #modelling: Ensemble selection from libraries of models (RC, ANM, GC, AK).
KDD-2004-CaruanaN #analysis #data mining #empirical #learning #metric #mining #performance: Data mining in metric space: an empirical analysis of supervised learning performance criteria (RC, ANM), pp. 69–78.
ICML-2000-OSullivanLCB #algorithm #named #robust: FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness (JO, JL, RC, AB), pp. 703–710.
SIGIR-2000-BergerCCFM #statistics: Bridging the lexical chasm: statistical approaches to answer-finding (ALB, RC, DC, DF, VOM), pp. 192–199.
ICML-1996-Caruana #algorithm #learning #multi: Algorithms and Applications for Multitask Learning (RC), pp. 87–95.
ICML-1995-BalujaC #algorithm #search-based #standard: Removing the Genetics from the Standard Genetic Algorithm (SB, RC), pp. 38–46.
ICML-1994-CaruanaF: Greedy Attribute Selection (RC, DF), pp. 28–36.
ICML-1993-Caruana #bias #induction #knowledge-based #learning #multi: Multitask Learning: A Knowledge-Based Source of Inductive Bias (RC), pp. 41–48.
ML-1989-CaruanaSE #algorithm #bias #induction #multi #search-based #using: Using Multiple Representations to Improve Inductive Bias: Gray and Binary Coding for Genetic Algorithms (RC, JDS, LJE), pp. 375–378.
ML-1988-CaruanaS #algorithm #bias #representation #search-based: Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms (RC, JDS), pp. 153–161.