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Open Knowledge
XHTML 1.0 W3C Rec
CSS 2.1 W3C CanRec
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Travelled to:
1 × Australia
1 × France
1 × Germany
1 × India
1 × Israel
1 × United Kingdom
3 × China
7 × USA
Collaborated with:
C.Guestrin J.Leskovec Y.Chen J.W.Burdick J.Djolonga M.Zuluaga M.Püschel R.Gomes V.Cevher H.P.Vanchinathan A.Karbasi O.Bachem M.Lucic V.Raychev M.T.Vechev B.Chen R.M.Castro T.Desautels M.Gomez-Rodriguez A.P.Singh Y.Sui A.Gotovos A.Badanidiyuru B.Mirzasoleiman I.Nikolic F.D.Bona G.Sergent P.A.Milder N.Srinivas S.Kakade M.W.Seeger A.Marfurt C.Robelin D.Kossmann A.Singla I.Bogunovic G.Bartók H.Shioi C.F.Montesinos L.P.Koh S.Wich C.Faloutsos J.M.VanBriesen N.S.Glance
Talks about:
optim (10) gaussian (7) process (7) submodular (4) explor (4) learn (4) activ (4) data (4) exploit (3) near (3)

Person: Andreas Krause

DBLP DBLP: Krause:Andreas

Contributed to:

ICML 20152015
KDD 20152015
POPL 20152015
ICML c1 20142014
ICML c2 20142014
KDD 20142014
RecSys 20142014
ICML c1 20132013
ICML 20122012
LCTES 20122012
ICML 20102010
KDD 20102010
ICML 20072007
KDD 20072007
ICML 20062006
ICML 20052005

Wrote 22 papers:

ICML-2015-BachemLK #estimation #parametricity
Coresets for Nonparametric Estimation — the Case of DP-Means (OB, ML, AK), pp. 209–217.
ICML-2015-DjolongaK #modelling #scalability
Scalable Variational Inference in Log-supermodular Models (JD, AK), pp. 1804–1813.
ICML-2015-SuiGBK #optimisation #process
Safe Exploration for Optimization with Gaussian Processes (YS, AG, JWB, AK), pp. 997–1005.
Discovering Valuable items from Massive Data (HPV, AM, CAR, DK, AK), pp. 1195–1204.
POPL-2015-RaychevVK #predict
Predicting Program Properties from “Big Code” (VR, MTV, AK), pp. 111–124.
ICML-c1-2014-ChenSMKWK #adaptation #detection
Active Detection via Adaptive Submodularity (YC, HS, CFM, LPK, SW, AK), pp. 55–63.
ICML-c2-2014-SinglaBBKK #education
Near-Optimally Teaching the Crowd to Classify (AS, IB, GB, AK, AK), pp. 154–162.
KDD-2014-BadanidiyuruMKK #on the fly #streaming #summary
Streaming submodular maximization: massive data summarization on the fly (AB, BM, AK, AK), pp. 671–680.
RecSys-2014-VanchinathanNBK #process #recommendation
Explore-exploit in top-N recommender systems via Gaussian processes (HPV, IN, FDB, AK), pp. 225–232.
ICML-c1-2013-ChenK #adaptation #learning #optimisation
Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization (YC, AK), pp. 160–168.
ICML-c1-2013-ZuluagaSKP #learning #multi #optimisation
Active Learning for Multi-Objective Optimization (MZ, GS, AK, MP), pp. 462–470.
ICML-2012-ChenCK #optimisation #process
Joint Optimization and Variable Selection of High-dimensional Gaussian Processes (BC, RMC, AK), p. 179.
ICML-2012-DesautelsKB #optimisation #process #trade-off
Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization (TD, AK, JWB), p. 109.
LCTES-2012-ZuluagaKMP #design #predict
“Smart” design space sampling to predict Pareto-optimal solutions (MZ, AK, PAM, MP), pp. 119–128.
ICML-2010-GomesK #data type #learning #parametricity
Budgeted Nonparametric Learning from Data Streams (RG, AK), pp. 391–398.
ICML-2010-KrauseC #representation #taxonomy
Submodular Dictionary Selection for Sparse Representation (AK, VC), pp. 567–574.
ICML-2010-SrinivasKKS #design #optimisation #process
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design (NS, AK, SK, MWS), pp. 1015–1022.
KDD-2010-Gomez-RodriguezLK #network
Inferring networks of diffusion and influence (MGR, JL, AK), pp. 1019–1028.
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.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
Hosted as a part of SLEBOK on GitHub.