BibSLEIGH — Andreas

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: Krause:Andreas

Contributed to:

2015

2014

2013

2012

2010

2007

2006

2005

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.
KDD-2015-VanchinathanMRK: 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.