`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:

### 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.