BibSLEIGH — Carlos

Travelled to:

1 × Australia
1 × Canada
1 × Germany
1 × Israel
1 × United Kingdom
2 × China
2 × France
9 × USA

Collaborated with:

A.Krause D.Shahaf K.El-Arini A.Kyrola J.K.Bradley D.Bickson E.B.Fox T.Johnson ∅ J.Huang J.Leskovec Y.Low J.M.Hellerstein T.Chen Y.Yue S.A.Hong E.Horvitz G.E.Blelloch A.P.Singh M.G.Lagoudakis R.Parr R.Patrascu D.Schuurmans M.Xu G.Veda B.Taskar V.Chatalbashev D.Koller J.E.Gonzalez H.Gu A.Deshpande S.Madden W.Hong J.Gonzalez C.Faloutsos J.M.VanBriesen N.S.Glance

Talks about:

learn (8) graph (6) explor (3) scale (3) model (3) distribut (2) algorithm (2) reinforc (2) parallel (2) hierarch (2)

Person: Carlos Guestrin

DBLP: Guestrin:Carlos

Contributed to:

2015

2014

2013

2012

2011

2010

2009

2007

2006

2005

2004

2002

Wrote 23 papers:

ICML-2015-JohnsonG #named #optimisation #scalability: Blitz: A Principled Meta-Algorithm for Scaling Sparse Optimization (TJ, CG), pp. 1171–1179.
ICML-c2-2014-ChenFG #monte carlo #probability: Stochastic Gradient Hamiltonian Monte Carlo (TC, EBF, CG), pp. 1683–1691.
CIKM-2013-Guestrin #machine learning #scalability #usability: Usability in machine learning at scale with graphlab (CG), pp. 5–6.
KDD-2013-El-AriniXFG #documentation #representation: Representing documents through their readers (KEA, MX, EBF, CG), pp. 14–22.
ICML-2012-YueHG: Hierarchical Exploration for Accelerating Contextual Bandits (YY, SAH, CG), p. 128.
KDD-2012-ShahafGH: Metro maps of science (DS, CG, EH), pp. 1122–1130.
OSDI-2012-GonzalezLGBG #distributed #graph #named: PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs (JEG, YL, HG, DB, CG), pp. 17–30.
OSDI-2012-KyrolaBG #graph #named #scalability: GraphChi: Large-Scale Graph Computation on Just a PC (AK, GEB, CG), pp. 31–46.
VLDB-2012-LowGKBGH #distributed #framework #in the cloud #machine learning: Distributed GraphLab: A Framework for Machine Learning in the Cloud (YL, JG, AK, DB, CG, JMH), pp. 716–727.
ICML-2011-BradleyKBG #coordination #parallel: Parallel Coordinate Descent for L1-Regularized Loss Minimization (JKB, AK, DB, CG), pp. 321–328.
KDD-2011-El-AriniG #keyword: Beyond keyword search: discovering relevant scientific literature (KEA, CG), pp. 439–447.
ICML-2010-BradleyG #learning #random: Learning Tree Conditional Random Fields (JKB, CG), pp. 127–134.
ICML-2010-HuangG #independence #learning #ranking: Learning Hierarchical Riffle Independent Groupings from Rankings (JH, CG), pp. 455–462.
KDD-2010-ShahafG: Connecting the dots between news articles (DS, CG), pp. 623–632.
KDD-2009-El-AriniVSG: Turning down the noise in the blogosphere (KEA, GV, DS, CG), pp. 289–298.
ICML-2007-KrauseG #approach #learning #process: Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach (AK, CG), pp. 449–456.
KDD-2007-LeskovecKGFVG #detection #effectiveness #network: Cost-effective outbreak detection in networks (JL, AK, CG, CF, JMV, NSG), pp. 420–429.
ICML-2006-KrauseLG #topic: Data association for topic intensity tracking (AK, JL, CG), pp. 497–504.
ICML-2005-GuestrinKS #process: Near-optimal sensor placements in Gaussian processes (CG, AK, APS), pp. 265–272.
ICML-2005-TaskarCKG #approach #learning #modelling #predict #scalability: Learning structured prediction models: a large margin approach (BT, VC, DK, CG), pp. 896–903.
VLDB-2004-DeshpandeGMHH #modelling #network: Model-Driven Data Acquisition in Sensor Networks (AD, CG, SM, JMH, WH), pp. 588–599.
ICML-2002-GuestrinLP #coordination #learning: Coordinated Reinforcement Learning (CG, MGL, RP), pp. 227–234.
ICML-2002-GuestrinPS #learning #modelling: Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs (CG, RP, DS), pp. 235–242.