BibSLEIGH — Jeff_G

Travelled to:

1 × Canada
1 × Finland
1 × Israel
1 × Slovenia
1 × United Kingdom
2 × France
9 × USA

Collaborated with:

A.W.Moore B.Póczos K.Das T.Huang Y.Zhang B.Bryan J.A.Boyan R.Garnett K.Kandasamy J.B.Oliva M.Tesch H.Choset D.J.Sutherland X.Wang Z.Ghahramani P.Donmez J.G.Carbonell D.B.Neill K.Deng H.B.McMahan C.M.Schafer J.Kubica D.Cohn W.Wong M.A.Riedmiller M.S.Lee Y.Krishnamurthy X.Xiong R.P.Mann

Talks about:

learn (6) function (5) activ (5) base (4) distribut (3) search (3) effici (3) optim (3) bayesian (2) without (2)

Person: Jeff G. Schneider

DBLP: Schneider:Jeff_G=

Contributed to:

2015

2013

2012

2010

2009

2008

2007

2004

2003

1999

1998

1997

Wrote 22 papers:

ICML-2015-KandasamySP #modelling #optimisation: High Dimensional Bayesian Optimisation and Bandits via Additive Models (KK, JGS, BP), pp. 295–304.
ICML-c3-2013-HuangS #learning #markov #modelling: Spectral Learning of Hidden Markov Models from Dynamic and Static Data (TKH, JGS), pp. 630–638.
ICML-c3-2013-OlivaPS: Distribution to Distribution Regression (JBO, BP, JGS), pp. 1049–1057.
ICML-c3-2013-TeschSC #optimisation #probability: Expensive Function Optimization with Stochastic Binary Outcomes (MT, JGS, HC), pp. 1283–1291.
KDD-2013-SutherlandPS #learning #matrix #rank: Active learning and search on low-rank matrices (DJS, BP, JGS), pp. 212–220.
KDD-2013-WangGS #graph: Active search on graphs (XW, RG, JGS), pp. 731–738.
ICML-2012-GarnettKXSM: Bayesian Optimal Active Search and Surveying (RG, YK, XX, JGS, RPM), p. 111.
ICML-2012-PoczosGS #dependence #kernel #metric: Copula-based Kernel Dependency Measures (BP, ZG, JGS), p. 213.
ICML-2012-ZhangS: Maximum Margin Output Coding (YZ, JGS), p. 53.
ICML-2010-ZhangS #reduction: Projection Penalties: Dimension Reduction without Loss (YZ, JGS), pp. 1223–1230.
ICML-2009-HuangS #learning #linear #sequence: Learning linear dynamical systems without sequence information (TKH, JGS), pp. 425–432.
KDD-2009-DonmezCS #learning: Efficiently learning the accuracy of labeling sources for selective sampling (PD, JGC, JGS), pp. 259–268.
ICML-2008-BryanS #learning: Actively learning level-sets of composite functions (BB, JGS), pp. 80–87.
KDD-2008-DasSN #category theory #dataset #detection: Anomaly pattern detection in categorical datasets (KD, JGS, DBN), pp. 169–176.
ICML-2007-BryanMSS: Efficiently computing minimax expected-size confidence regions (BB, HBM, CMS, JGS), pp. 97–104.
KDD-2007-DasS #category theory #dataset #detection: Detecting anomalous records in categorical datasets (KD, JGS), pp. 220–229.
KDD-2004-DasMS: Belief state approaches to signaling alarms in surveillance systems (KD, AWM, JGS), pp. 539–544.
ICML-2003-KubicaMCS #analysis #collaboration #graph #performance #query: Finding Underlying Connections: A Fast Graph-Based Method for Link Analysis and Collaboration Queries (JK, AWM, DC, JGS), pp. 392–399.
ICML-1999-SchneiderWMR #distributed: Distributed Value Functions (JGS, WKW, AWM, MAR), pp. 371–378.
ICML-1998-MooreSBL #learning #named #optimisation: Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions (AWM, JGS, JAB, MSL), pp. 386–394.
ICML-1998-SchneiderBM #scheduling: Value Function Based Production Scheduling (JGS, JAB, AWM), pp. 522–530.
ICML-1997-MooreSD #performance #polynomial #predict: Efficient Locally Weighted Polynomial Regression Predictions (AWM, JGS, KD), pp. 236–244.