BibSLEIGH — Daphne

Travelled to:

1 × Australia
1 × Germany
1 × Greece
1 × Israel
1 × Italy
1 × United Kingdom
14 × USA
3 × Canada

Collaborated with:

B.Taskar J.Y.Halpern ∅ H.Wang M.Sahami A.Y.Ng V.Chatalbashev N.Megiddo A.J.Grove D.Vickrey T.Gao S.Tong R.Fratkina M.P.Kumar B.Packer V.Jojic S.Gould C.C.Lin R.Raina M.F.Wong U.Chajewska D.Ormoneit D.Florescu A.Y.Levy B.v.Stengel S.Lee C.Guestrin L.Getoor N.Friedman

Talks about:

learn (12) use (4) featur (3) relat (3) model (3) data (3) probabilist (2) construct (2) algorithm (2) structur (2)

Person: Daphne Koller

DBLP: Koller:Daphne

Contributed to:

2015

2013

2012

2011

2010

2007

2006

2005

2004

2003

2001

2000

1998

1997

1996

1994

1993

1992

Wrote 26 papers:

KDD-2015-Koller #named #question #what: MOOCS: What Have We Learned? (DK), p. 3.
ICML-c2-2013-WangK13a #approach #message passing #problem: Subproblem-Tree Calibration: A Unified Approach to Max-Product Message Passing (HW, DK), pp. 190–198.
ICML-c3-2013-WangK #algorithm #energy #performance: A Fast and Exact Energy Minimization Algorithm for Cycle MRFs (HW, DK), pp. 190–198.
KDD-2013-NgK #education #online: The online revolution: education for everyone (AYN, DK), p. 2.
ICML-2012-KumarPK #learning #modelling #nondeterminism: Modeling Latent Variable Uncertainty for Loss-based Learning (MPK, BP, DK), p. 29.
ICML-2011-GaoK #multi: Multiclass Boosting with Hinge Loss based on Output Coding (TG, DK), pp. 569–576.
ICML-2010-JojicGK #composition: Accelerated dual decomposition for MAP inference (VJ, SG, DK), pp. 503–510.
ICML-2010-VickreyLK: Non-Local Contrastive Objectives (DV, CCYL, DK), pp. 1103–1110.
ICML-2007-LeeCVK #learning #multi: Learning a meta-level prior for feature relevance from multiple related tasks (SIL, VC, DV, DK), pp. 489–496.
ICML-2006-RainaNK #learning #using: Constructing informative priors using transfer learning (RR, AYN, DK), pp. 713–720.
ICML-2005-TaskarCKG #approach #learning #modelling #predict #scalability: Learning structured prediction models: a large margin approach (BT, VC, DK, CG), pp. 896–903.
ICML-2004-TaskarCK #learning #markov #network: Learning associative Markov networks (BT, VC, DK).
ICML-2003-TaskarWK #learning #testing: Learning on the Test Data: Leveraging Unseen Features (BT, MFW, DK), pp. 744–751.
KDD-2003-Koller #learning #relational #statistics: Statistical learning from relational data (DK), p. 4.
ICML-2001-ChajewskaKO #behaviour #learning: Learning an Agent’s Utility Function by Observing Behavior (UC, DK, DO), pp. 35–42.
ICML-2001-GetoorFKT #learning #modelling #probability #relational: Learning Probabilistic Models of Relational Structure (LG, NF, DK, BT), pp. 170–177.
ICML-2000-TongK #classification #learning: Support Vector Machine Active Learning with Application sto Text Classification (ST, DK), pp. 999–1006.
ICML-1998-KollerF #approximate #learning #probability #process #using: Using Learning for Approximation in Stochastic Processes (DK, RF), pp. 287–295.
ICML-1997-KollerS #documentation #using #word: Hierarchically Classifying Documents Using Very Few Words (DK, MS), pp. 170–178.
VLDB-1997-FlorescuKLP #integration #probability #using: Using Probabilistic Information in Data Integration (DF, DK, AYL), pp. 216–225.
ICML-1996-KollerS #feature model #towards: Toward Optimal Feature Selection (DK, MS), pp. 284–292.
STOC-1994-KollerMS #algorithm #game studies #performance #random: Fast algorithms for finding randomized strategies in game trees (DK, NM, BvS), pp. 750–759.
STOC-1993-KollerM #constraints: Constructing small sample spaces satisfying given constraints (DK, NM), pp. 268–277.
KR-1992-KollerH #approximate #logic #reasoning: A Logic for Approximate Reasoning (DK, JYH), pp. 153–164.
LICS-1992-GroveHK #random: Random Worlds and Maximum Entropy (AJG, JYH, DK), pp. 22–33.
STOC-1992-GroveHK #first-order #logic: Asymptotic Conditional Probabilities for First-Order Logic (AJG, JYH, DK), pp. 294–305.