BibSLEIGH — Thomas_G

Travelled to:

1 × Australia
1 × Finland
1 × Germany
1 × Italy
1 × Turkey
1 × United Kingdom
12 × USA
2 × Canada
2 × China

Collaborated with:

D.D.Margineantu ∅ P.Tadepalli L.Liu D.Sheldon W.Zhang P.Wu G.Valentini X.Wang V.B.Zubek E.Chown N.S.Flann E.B.Kong H.Almuallim G.Cerbone R.A.Ruff A.Fern A.Ashenfelter Y.Bulatov T.Fountain B.Sudyka M.J.Kearns Y.Mansour H.Hild G.Bakiri T.Sun A.Kumar A.Surve X.Fern N.Mehta S.Ray H.Deng E.N.Mortensen D.Busquets R.L.d.Mántaras C.Sierra C.Jensen H.Lonsdale E.Wynn J.Cao M.Slater N.Larios B.Soran L.G.Shapiro G.Martínez-Muñoz J.Lin S.Natarajan E.Altendorf A.C.Restificar Ethan W. Dereszynski Jesse Hostetler Thao-Trang Hoang Mark Udarbe S.A.Chien B.L.Whitehall R.J.Doyle B.Falkenhainer J.Garrett S.C.Y.Lu T.E.Senator H.G.Goldberg A.Memory W.T.Young B.Rees R.Pierce D.Huang M.Reardon D.A.Bader E.Chow I.A.Essa J.Jones V.Bettadapura D.H.Chau O.Green O.Kaya A.Zakrzewska E.Briscoe R.L.M.IV R.McColl L.Weiss W.Wong S.Das A.Emmott J.Irvine J.Y.Lee D.Koutra C.Faloutsos D.D.Corkill L.Friedland A.Gentzel D.Jensen

Talks about:

learn (16) reinforc (5) model (5) base (4) hierarch (3) improv (3) probabilist (2) knowledg (2) gradient (2) classifi (2)

Person: Thomas G. Dietterich

DBLP: Dietterich:Thomas_G=

Contributed to:

2014

2013

2010

2009

2008

2006

2005

2004

2003

2002

2000

1998

1997

1996

1995

1992

1991

1990

1989

2011

Wrote 32 papers:

ICML-c2-2014-LiuD #learning #problem #set: Learnability of the Superset Label Learning Problem (LPL, TGD), pp. 1629–1637.
ICML-c2-2014-LiuSD #approximate #modelling #visual notation: Gaussian Approximation of Collective Graphical Models (LPL, DS, TGD), pp. 1602–1610.
ICML-c3-2013-SheldonSKD #approximate #modelling #visual notation: Approximate Inference in Collective Graphical Models (DS, TS, AK, TGD), pp. 1004–1012.
KDD-2013-SenatorGMYRPHRBCEJBCGKZBMMWDFWDEILKFCFGJ #database #detection #process: Detecting insider threats in a real corporate database of computer usage activity (TES, HGG, AM, WTY, BR, RP, DH, MR, DAB, EC, IAE, JJ, VB, DHC, OG, OK, AZ, EB, RLMI, RM, LW, TGD, AF, WKW, SD, AE, JI, JYL, DK, CF, DDC, LF, AG, DJ), pp. 1393–1401.
CHI-2010-JensenLWCSD #case study: The life and times of files and information: a study of desktop provenance (CJ, HL, EW, JC, MS, TGD), pp. 767–776.
ICPR-2010-LariosSSMLD #identification #kernel #random: Haar Random Forest Features and SVM Spatial Matching Kernel for Stonefly Species Identification (NL, BS, LGS, GMM, JL, TGD), pp. 2624–2627.
ICML-2009-ZhangSFD #learning: Learning non-redundant codebooks for classifying complex objects (WZ, AS, XF, TGD), pp. 1241–1248.
ICML-2008-MehtaRTD #automation: Automatic discovery and transfer of MAXQ hierarchies (NM, SR, PT, TGD), pp. 648–655.
ICPR-v1-2006-ZhangDDM #multi #recognition: A Hierarchical Object Recognition System Based on Multi-scale Principal Curvature Regions (WZ, HD, TGD, ENM), pp. 778–782.
ICML-2005-NatarajanTADFR #first-order #learning #modelling #probability: Learning first-order probabilistic models with combining rules (SN, PT, EA, TGD, AF, ACR), pp. 609–616.
ICML-2004-DietterichAB #random: Training conditional random fields via gradient tree boosting (TGD, AA, YB).
ICML-2004-WuD #data flow: Improving SVM accuracy by training on auxiliary data sources (PW, TGD).
ICML-2003-ValentiniD #bias: Low Bias Bagged Support Vector Machines (GV, TGD), pp. 752–759.
ICML-2003-WangD #learning #modelling #policy: Model-based Policy Gradient Reinforcement Learning (XW, TGD), pp. 776–783.
ICML-2002-DietterichBMS #learning #probability #refinement: Action Refinement in Reinforcement Learning by Probability Smoothing (TGD, DB, RLdM, CS), pp. 107–114.
ICML-2002-ZubekD #heuristic #learning: Pruning Improves Heuristic Search for Cost-Sensitive Learning (VBZ, TGD), pp. 19–26.
ICML-2000-ChownD #approach #divide and conquer #information management #learning: A Divide and Conquer Approach to Learning from Prior Knowledge (EC, TGD), pp. 143–150.
ICML-2000-MargineantuD #classification #evaluation: Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers (DDM, TGD), pp. 583–590.
KDD-2000-FountainDS #mining #testing: Mining IC test data to optimize VLSI testing (TF, TGD, BS), pp. 18–25.
ICML-1998-Dietterich #learning: The MAXQ Method for Hierarchical Reinforcement Learning (TGD), pp. 118–126.
ICML-1997-MargineantuD #adaptation: Pruning Adaptive Boosting (DDM, TGD), pp. 211–218.
ICML-1997-TadepalliD #learning: Hierarchical Explanation-Based Reinforcement Learning (PT, TGD), pp. 358–366.
ICML-1996-DietterichKM #framework #learning: Applying the Waek Learning Framework to Understand and Improve C4.5 (TGD, MJK, YM), pp. 96–104.
ICML-1995-DietterichF #learning #perspective: Explanation-Based Learning and Reinforcement Learning: A Unified View (TGD, NSF), pp. 176–184.
ICML-1995-KongD #bias: Error-Correcting Output Coding Corrects Bias and Variance (EBK, TGD), pp. 313–321.
ML-1992-AlmuallimD #concept #learning #on the: On Learning More Concepts (HA, TGD), pp. 11–19.
ML-1991-CerboneD #compilation #optimisation: Knowledge Compilation to Speed Up Numerical Optimization (GC, TGD), pp. 600–604.
ML-1991-ChienWDDFGL #automation #machine learning: Machine Learning in Engineering Automation (SAC, BLW, TGD, RJD, BF, JG, SCYL), pp. 577–580.
ML-1990-DietterichHB #case study #comparative: A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping (TGD, HH, GB), pp. 24–31.
ML-1989-Dietterich #induction #learning: Limitations on Inductive Learning (TGD), pp. 124–128.
ML-1989-RuffD #question #what: What Good Are Experiments? (RAR, TGD), pp. 109–112.
AIIDE-2011-DereszynskiHFDHU #behaviour #game studies #learning #modelling #probability #realtime: Learning Probabilistic Behavior Models in Real-Time Strategy Games (EWD, JH, AF, TGD, TTH, MU).