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Ackermann, Chris
Aggarwal, Charu
Amantullah, Brian
Arredondo, Jaime
Augustine, Eriq
Augustine*, Eriq
Babaki, Behrouz
Babaki, Behrouz
Bach, Stephen H.
Barash, Vladimir
Beard, Mitchell
Bengio, Yoshua
Bhattacharya, Indrajit
Bilgic, Mustafa
Blake, Brian
Blondeel, Marjon
Borgatti, Steve
Boyd-Graber, Jordan
Bradley, Skaggs
Broecheler, Matthias
Brownstein, John
Butler, Patrick
Cadena, Jose
Carlson, Bjorn
Carstea, Eugene
Chajewska, Ursulza
Chang, Johnnie
Chen, Daozheng
Chen, Yunfei
Chen, Robert
Choi, Jonghyun
Cohen, William
Cook, D.
Daume-III, Hal
Davis, Larry
De Cock, Martine
Deshpande, Amol
Diehl, Christopher
Dietterich, Thomas
Djeraba, Chabane
Domingos, Pedro
Dong, Xin Luna
Doppa, Janardhan
Doyle, Andy
Dzeroski, S.
Eirinaki, Magdalini
Eliassi-Rad, Tina
Elsayed, Tamer
Embar, Varun
Embar*, Varun
Fakhraei, Shobeir
Faloutsos, Christos
Farnadi, Golnoosh
Farnadi, Golnoosh
Fayed, Youssef
Feldman, Ronen
Ford, Jim
Foulds, James
Friedman, Nir
Friedman, Mark
Fromherz, Markus
Fuxman, Ariel
Gallagher, Brian
Gazen, Bora C.
Getoor, Lise
Getoor, Lise
Ghosh, Saurav
Golbeck, Jennifer
Goldwasser, Dan
Grant, John
Grossman, Robert
Grycner, Adam
Guiver, John
Gupta, Dipak
Haidarian-Shahri, Hamid
Halgin, Daniel
He, Xinran
Healy, Patrick
Holder, L.
Hollis, Victoria
Huang, Bert
Hung, Edward
Hwang, Heasoo
III, Hal Daume
Islamaj, Rezarta
Isley, Steve
Jacobs, David
Jr., Nick Short
Kang, Jeonhyung
Kang, Hyunmo
Katz, Graham
Khamis, Sameh
Khandpur, Rupinder
Kim, Jihie
Kim, Sungchul
Kimmig, Angelika
Kimmig, Angelika
Kini, Nikhil
Knoblock, Craig
Koehly, Laura
Koh, Eunyee
Kok, Stanley
Kolcz, Alek
Koller, Daphne
Korkmaz, Gizem
Kouki, Pigi
Kouki, Pigi
Kuhlman, Christopher
Kumar, Shachi
Kuter, Ugur
Lansky, Amy
Lauw, Hady
Lavedan, Christian
Lavrac, N.
Lerman, Kristina
Licamele, Louis
Liu, Xiangyang
Liu, Huan
Liu, Yan
London, Ben
Lu, Qing
Machanavajjhala, Ashwin
Mack, Kendra
Macskassy, Sofus
Mann, Janet
Marathe, Achla
Marcum, Christopher
Mares, David
Maulik, U.
Mekaru, Sumiko
Memory, Alex
Memory, Alex
Miao, Hui
Michelson, Matthew
Mihalkova, Lilyana
Milic-Frayling, Natasa
Miller, Renee
Miller, Renee J
Minton, Steve
Minton, Steven
Mitkus, Shruti
Moens, Marie-Francine
Motoda, Hiroshi
Mount, Stephen
Moustafa, Walaa Eldin
Muggleton, Stephen
Muthiah, Sathappan
Namata, Galileo Mark
Navlakha, Saket
Nikolov, Nikola S.
Norman, Joseph
Norris, Eric
Norton, Myra
Nsoesie, Elaine
Ntoulas, Alexcandros
O'Leary, Dianne P.
ODonovan, John
Oard, Doug
Onukwugha, Eberechukwu
Ottosson, Gregor
Papadimitriou, Panagiotis
Pfeffer, Avi
Piatetsky-Shapiro, Gregory
Plangprasopchok, Anon
Polymeropoulos, Mihales
Polyzotis, Neoklis
Pujara, Jay
Pujara, Jay
Pujra, Jay
Ramakrishnan, Naren
Ramesh, Arti
Ramesh, Arti
Rand, William
Rao, Nikhil S
Raschid, Louiqa
Rastegari, Mohammad
Rathod, Priyang
Reddy, Charu C. Aggarwa
Rekatsinas, Theodoros
Rhee, Jeanne
Riloff, Ellen
Rodrigues, Eduarda Mendes
Rodriguez, Mario
Roussopoulos, Nick
Saha, Barna
Sahami, Mehran
Saraf, Parang
Sarawagi, Sunita
Sayyadi, Hassan
Schaffer, James
Scheffer, Tobias
Schmidler, Scott
Schnaitter, Karl
See, Kane
Segal, Eran
Sehgal, Vivek
Self, Nathan
Sen, Prithviraj
Shahar, Yuval
Sharara, Hossam
Shashanka, Madhusudana
Shen, Shitian
Shneiderman, Ben
Singh, Lisa
Skomoroch, Peter
Small, Peter
Smith, Marc
Somasundaran, Swapna
Sopan, Awalin
Springer, Aaron
Sridhar, Dhanya
Srinivasan, Aravind
Srinivasan, Sriram
Srinivasan*, Sriram
Srivastava, Ashok
Srivastava, Divesh
Staats, Brian
Subbaian, Karthik
Subrahmanian, V. S.
Summers, Kristin
Tadepalli, Prasad
Taskar, Benjamin
Terzi, Evimaria
Thompson, Spencer K.
Ting, Hua
Tomkins, Sabina
Trinh, Khoa
Tsaparas, Panayiotis
Udrea, Octavian
Viechnicki, Peter
Volpi, Simona
Vullikanti, Anil
Walker, Marilyn
Wang, Wei
Weikum, Gerhard
Welser, Howard
Whittaker, Steve
Wiebe, Janyce
Wilbur, John
Yeh, Tom
Yoo, Jaebong
Yu, Jiawei Han Philip
Yu, Jun
Zaki, Mohammed
Zavorin, Ilya
Zhang, Yi
Zhang, Yue
Zhao, Liang
Zhao, Bin
Zheleva, Elena
desJardins, Marie
Type
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Conference Paper
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Book Chapter
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Year
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2010
2009
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2005
2004
2003
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2001
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1994
Keyword
any
Cognition
Comparative Analysis
Complexity theory
Data engineering
Discussion Forums
First-order probabilistic models
HL-MRFs
Knowledge engineering
LDA
Lifted inference and learning
MOOC
MOOC Discussion Forums
MOOCs
Metadata
Model Comparison
Online Courses
PAC-Bayes
Par-factor graphs
Probabilistic logic
Probabilistic programming
SRL
Schema mapping
Seeded LDA
Socio-behavioral models
Statistical relational learning
Task analysis
Templated graphical models
Uncertain Graphs
Visualizing Uncertainty
anonymity online
bioinformatics gene expression analysis antipsychotic pharmacogenetics
collective classification
collective mapping discovery
data integration
defect
feature generation
functional biological signals
gene expression bioinformatics drug therapeutics
generalization bounds
groups
high school MOOCs
inference mechanisms
influence
latent variable models
learner engagement
learning analytics
learning theory
meta data
online education
optimisation
optimization
potential mappings
privacy
probabilistic modeling
probabilistic reasoning techniques
probabilistic soft logic
probability
professional networks
schema mapping optimization problem
search
sensitive attribute inference
social media
social networks
splice-site
statistical relational language
structured prediction
student learning
tutorial
uncertainty handling
web
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Title
]
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A
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X
Y
Z
L
Getoor, L.
,
Friedman, N.
&
Koller, D.
Learning Structured Statistical Models from Relational Data
.
Electronic Transactions on Artificial Intelligence
6,
(2002).
Google Scholar
BibTex
Getoor, L.
Learning Statistical Models from Relational Data
. (2001).
Google Scholar
BibTex
getoor-thesis.pdf
(3.39 MB)
Getoor, L.
,
Koller, D.
,
Taskar, B.
&
Friedman, N.
Learning Probabilistic Relational Models with Structural Uncertainty
.
Proceedings of the AAAI Workshop on Learning Statistical Models from Relational Data
(2000).
Google Scholar
BibTex
Getoor, L.
,
Friedman, N.
,
Koller, D.
&
Pfeffer, A.
Relational Data Mining
(
Dzeroski, S.
&
Lavrac, N.
) (Springer-Verlag, 2001).
Google Scholar
BibTex
Friedman, N.
,
Getoor, L.
,
Koller, D.
&
Pfeffer, A.
Learning Probabilistic Relational Models
.
International Joint Conference on Articial Intelligence
(1999).
Google Scholar
BibTex
icjai99.pdf
(156.94 KB)
Getoor, L.
,
Friedman, N.
,
Koller, D.
&
Pfeffer, A.
Learning Probabilistic Relational Models
.
Relational Data Mining
(Springer-Verlag, 2001).
Google Scholar
BibTex
lprm-ch.pdf
(376 KB)
Getoor, L.
,
Friedman, N.
,
Koller, D.
&
Taskar, B.
Learning Probabilistic Models of Relational Structure
.
Proceedings of International Conference on Machine Learning (ICML)
(2001).
Google Scholar
BibTex
icml01.pdf
(157.91 KB)
Getoor, L.
,
Friedman, N.
,
Koller, D.
&
Taskar, B.
Learning Probabilistic Models of Link Structure
.
Journal of Machine Learning Research
3,
679- -707 (2002).
Google Scholar
BibTex
jmlr02.pdf
(502.22 KB)
Bach, S. H.
,
Huang, B.
&
Getoor, L.
Learning Latent Groups with Hinge-loss Markov Random Fields
.
ICML Workshop on Inferning: Interactions between Inference and Learning
(2013).
Google Scholar
BibTex
bach-inferning13.pdf
(348.79 KB)
Ramesh, A.
,
Goldwasser, D.
,
Huang, B.
,
III, H. Daume
&
Getoor, L.
Learning Latent Engagement Patterns of Students in Online Courses
.
Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence
(AAAI Press, 2014).
Google Scholar
BibTex
ramesh-aaai14.pdf
(505.47 KB)
Doppa, J.
,
Yu, J.
,
Tadepalli, P.
&
Getoor, L.
Learning Algorithms for Link Prediction based on Chance Constraints
.
European Conference on Machine Learning (ECML)
(2010).
Google Scholar
BibTex
doppa-ecml10.pdf
(203 KB)
Foulds, J.
,
Kumar, S.
&
Getoor, L.
Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models
.
International Conference on Machine Learning (ICML)
(2015).
Google Scholar
BibTex
Foulds2015LatentTopicNetworks.pdf
(382.53 KB)
Bhattacharya, I.
&
Getoor, L.
A Latent Dirichlet Model for Unsupervised Entity Resolution
.
SIAM Conference on Data Mining (SDM)
(2006).
Google Scholar
BibTex
bhattacharyasdm06.pdf
(209.24 KB)
Bach, S. H.
,
Huang, B.
&
Getoor, L.
Large-margin Structured Learning for Link Ranking
.
NIPS Workshop on Frontiers of Network Analysis: Methods, Models, and Applications
(2013).
Google Scholar
BibTex
bach-fna13.pdf
(210.09 KB)
Pujara, J.
,
Miao, H.
,
Getoor, L.
&
Cohen, W.
Large-Scale Knowledge Graph Identification using PSL
.
ICML Workshop on Structured Learning (SLG)
(2013).
Google Scholar
BibTex
pujara_slg13.pdf
(277.63 KB)
Pujara, J.
,
Miao, H.
,
Getoor, L.
&
Cohen, W.
Large-Scale Knowledge Graph Identification using PSL
.
AAAI Fall Symposium on Semantics for Big Data
(2013).
Google Scholar
BibTex
pujara_s4bd13.pdf
(306.96 KB)
Pujara, J.
&
Skomoroch, P.
Large-Scale Hierarchical Topic Models
.
NIPS Workshop on Big Learning
(2012).
Google Scholar
BibTex
pujara_biglearn12.pdf
(189.96 KB)
Kang, J.
,
Lerman, K.
&
Getoor, L.
LA-LDA: A Limited Attention Topic Model for Social Recommendation
.
The 2013 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction (SBP 2013)
(2013).
Google Scholar
BibTex
kang-sbp13.pdf
(622.52 KB)
K
Pujara, J.
,
Miao, H.
,
Getoor, L.
&
Cohen, W.
Knowledge Graph Identification
.
International Semantic Web Conference (ISWC)
(2013).
Google Scholar
BibTex
pujara_iswc13.pdf
(508.7 KB)
J
Sridhar, D.
&
Getoor, L.
Joint Probabilistic Inference of Causal Structure
.
22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining Workshop on Causal Discovery
(2016).
Google Scholar
BibTex
sridhar-kdd-ws16.pdf
(204.51 KB)
Sridhar, D.
,
Foulds, J.
,
Walker, M.
,
Huang, B.
&
Getoor, L.
Joint Models of Disagreement and Stance in Online Debate
.
Annual Meeting of the Association for Computational Linguistics (ACL)
(2015).
Google Scholar
BibTex
sridhar-acl15.pdf
(227.14 KB)
Pujara, J.
,
Miao, H.
&
Getoor, L.
Joint Judgments with a Budget: Strategies for Reducing the Cost of Inference
.
ICML Workshop on Machine Learning with Test-Time Budgets
(2013).
Google Scholar
BibTex
pujara_wtbudg13.pdf
(221.26 KB)
I
Bhattacharya, I.
&
Getoor, L.
Iterative Record Linkage for Cleaning and Integration
.
ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD)
(2004).
Google Scholar
BibTex
bhattacharyasigmod04-wkshp.pdf
(222.38 KB)
Getoor, L.
&
Taskar, B.
Introduction to Statistical Relational Learning
. (The MIT Press, 2007).
Google Scholar
BibTex
Getoor, L.
An Introduction to Probabilistic Graphical Models for Relational Data
.
Data Engineering Bulletin
29,
(2006).
Google Scholar
BibTex
Ramesh, A.
,
Goldwasser, D.
,
Huang, B.
,
Daume-III, H.
&
Getoor, L.
Interpretable Engagement Models for MOOCs using Hinge-loss Markov Random Fields
.
Transactions on Learning Technologies
(2019).
Google Scholar
BibTex
ramesh-tlt19.pdf
(4.3 MB)
Kang, H.
,
Getoor, L.
,
Shneiderman, B.
,
Bilgic, M.
&
Licamele, L.
Interactive Entity Resolution in Relational Data: A Visual Analytic Tool and Its Evaluation
.
IEEE Transactions on Visualization and Computer Graphics
14,
999–1014 (2008).
Google Scholar
BibTex
kang-tvcg08.pdf
(3.63 MB)
Bilgic, M.
Information Acquisition in Structured Domains
. (2010).
Google Scholar
BibTex
mbilgic-phdthesis.pdf
(4.68 MB)
Namata, G. Mark
,
Getoor, L.
&
Diehl, C.
Inferring Organizational Titles in Online Communications
.
ICML Workshop on Statistical Network Analysis
(2006).
Google Scholar
BibTex
icml2006_ExtAbst.pdf
(72.39 KB)
Licamele, L.
&
Getoor, L.
Indirect two-sided relative ranking: a robust similarity measure for gene expression data
.
BMC Bioinformatics
(2010).
Google Scholar
BibTex
Schnaitter, K.
,
Polyzotis, N.
&
Getoor, L.
Index Interactions in Physical Design Tuning: Modeling, Analysis, and Applications
.
International Conference on Very Large Data Bases
(2009).
Google Scholar
BibTex
schnaitter-vldb09.pdf
(743.29 KB)
Singh, L.
&
Getoor, L.
Increasing the predictive power of affiliation networks.
IEEE Data Engineering Bulletin
30,
(2007).
Google Scholar
BibTex
singh.pdf
(87.94 KB)
Minton, S.
et al.
Improving Classifier Performance by Autonomously Collecting Background Knowledge from the Web
.
Tenth International Conference on Machine Learning and Applications
(2011).
Google Scholar
BibTex
minton-icmla2011.pdf
(733.09 KB)
London, B.
,
Huang, B.
&
Getoor, L.
Improved Generalization Bounds for Large-scale Structured Prediction
.
NIPS Workshop on Algorithmic and Statistical Approaches for Large Social Networks
(2012).
Google Scholar
BibTex
london-nips12ws.pdf
(213.95 KB)
Tomkins, S.
,
Farnadi, G.
,
Amantullah, B.
,
Getoor, L.
&
Minton, S.
The Impact of Environmental Stressors on Human Trafficking
.
Beyond Online Data (ICWSM Workshop)
(2018).
Google Scholar
BibTex
icdm_2018.pdf
(473.58 KB)
Tomkins, S.
,
Farnadi, G.
,
Amantullah, B.
,
Getoor, L.
&
Minton, S.
The Impact of Environmental Stressors on Human Trafficking
.
International Conference on Data Mining (ICDM)
(2018).
Google Scholar
BibTex
icdm_2018.pdf
(473.58 KB)
Namata, G. Mark
.
Identifying Graphs from Noisy Observational Data
. (2012).
Google Scholar
BibTex
namata-phdthesis.pdf
(1.51 MB)
Namata, G. Mark
&
Getoor, L.
Identifying Graphs From Noisy and Incomplete Data
.
1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data
(2009).
Google Scholar
BibTex
namatag-kddu09.pdf
(241.7 KB)
Srinivasan, S.
,
Rao, N. S.
,
Subbaian, K.
&
Getoor, L.
Identifying Facet Mismatches In Search Via Micrographs
.
The 28th ACM International Conference on Information and Knowledge Management
(2019).
Google Scholar
BibTex
srinivasan-cikm19.pdf
(887.06 KB)
H
Miao, H.
,
Liu, X.
,
Huang, B.
&
Getoor, L.
A Hypergraph-Partitioned Vertex Programming Approach for Large-scale Consensus Optimization
.
2013 IEEE International Conference on Big Data
(2013).
Google Scholar
BibTex
miao-bd13.pdf
(307.51 KB)
Kouki, P.
,
Fakhraei, S.
,
Foulds, J.
,
Eirinaki, M.
&
Getoor, L.
HyPER: A Flexible and Extensible Probabilistic Framework for Hybrid Recommender Systems
.
9th ACM Conference on Recommender Systems (RecSys)
(ACM, 2015).
Google Scholar
BibTex
kouki-recsys15.pdf
(1.03 MB)
Bach, S. H.
,
Huang, B.
,
London, B.
&
Getoor, L.
Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction
.
Uncertainty in Artificial Intelligence
(2013).
Google Scholar
BibTex
bach-uai13.pdf
(379.45 KB)
Bach, S. H.
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic: A Scalable Approach to Structured Prediction
. (2015).
Google Scholar
BibTex
bach-thesis15.pdf
(1.17 MB)
Bach, S. H.
,
Broecheler, M.
,
Huang, B.
&
Getoor, L.
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic
.
Journal of Machine Learning Research (JMLR)
18,
1-67 (2017).
Google Scholar
BibTex
bach-jmlr17.pdf
(731.56 KB)
Bach, S. H.
,
Broecheler, M.
,
Huang, B.
&
Getoor, L.
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic
.
ArXiv:1505.04406 [cs.LG]
(2015).
Google Scholar
BibTex
bach-arxiv15.pdf
(686.27 KB)
Zheleva, E.
,
Getoor, L.
&
Sarawagi, S.
Higher-order Graphical Models for Classification in Social and Affiliation Networks
.
NIPS Workshop on Networks Across Disciplines: Theory and Applications
(2010).
Google Scholar
BibTex
zheleva-nips2010.pdf
(200.43 KB)
He, X.
,
Rekatsinas, T.
,
Foulds, J.
,
Getoor, L.
&
Liu, Y.
HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades
.
International Conference on Machine Learning
(2015).
Google Scholar
BibTex
He2015HawkesTopic.pdf
(819.91 KB)
Udrea, O.
,
Miller, R.
&
Getoor, L.
HOMER: Ontology visualization and analysis
.
Demo Presentation at International Semantic Web Conference (ISWC)
(2007).
Google Scholar
BibTex
homer.pdf
(125.38 KB)
Udrea, O.
,
Getoor, L.
&
Miller, R.
HOMER: Ontology Alignment Visualization and Analysis
. (2007).
Google Scholar
BibTex
getoor-homer07.pdf
(125.38 KB)
G
Plangprasopchok, A.
,
Lerman, K.
&
Getoor, L.
Growing a tree in the forest: constructing folksonomies by integrating structured metadata
.
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
(2010).
Google Scholar
BibTex
plang-kdd10.pdf
(705.71 KB)
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