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ORCIDLise Getoor
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- affiliation: University of California, Santa Cruz, Department of Computer Science, CA, USA
- affiliation: University of Maryland, College Park, USA
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2020 – today
- 2024
[c178]Charles Dickens, Connor Pryor, Lise Getoor:
Modeling Patterns for Neural-Symbolic Reasoning Using Energy-based Models. AAAI Spring Symposia 2024: 90-99- [c177]Charles Andrew Dickens, Changyu Gao, Connor Pryor, Stephen J. Wright, Lise Getoor:
Convex and Bilevel Optimization for Neural-Symbolic Inference and Learning. ICML 2024 - [i43]Charles Dickens, Changyu Gao, Connor Pryor, Stephen J. Wright, Lise Getoor:
Convex and Bilevel Optimization for Neuro-Symbolic Inference and Learning. CoRR abs/2401.09651 (2024) - [i42]Connor Pryor, Quan Yuan, Jeremiah Z. Liu, Mehran Kazemi, Deepak Ramachandran, Tania Bedrax-Weiss, Lise Getoor:
Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic. CoRR abs/2403.17853 (2024) - [i41]Charles Dickens, Connor Pryor, Changyu Gao, Alon Albalak, Eriq Augustine, William Yang Wang, Stephen J. Wright, Lise Getoor:
A Mathematical Framework, a Taxonomy of Modeling Paradigms, and a Suite of Learning Techniques for Neural-Symbolic Systems. CoRR abs/2407.09693 (2024) - 2023
- [j65]Eriq Augustine, Lise Getoor:
Collective Grounding: Applying Database Techniques to Grounding Templated Models. Proc. VLDB Endow. 16(8): 1843-1855 (2023) - [c176]Connor Pryor, Quan Yuan, Jeremiah Z. Liu, Mehran Kazemi, Deepak Ramachandran, Tania Bedrax-Weiss, Lise Getoor:
Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic. ACL (1) 2023: 7631-7652 - [c175]Yi-Lin Tuan, Alon Albalak, Wenda Xu, Michael Saxon
, Connor Pryor, Lise Getoor, William Yang Wang:
CausalDialogue: Modeling Utterance-level Causality in Conversations. ACL (Findings) 2023: 12506-12522 - [c174]Charles Dickens, Alexander Miller, Lise Getoor:
Online Collective Demand Forecasting for Bike Sharing Services. HICSS 2023: 1186-1194 - [c173]Kaiwen Zhou, Kaizhi Zheng, Connor Pryor, Yilin Shen, Hongxia Jin, Lise Getoor, Xin Eric Wang:
ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation. ICML 2023: 42829-42842 - [c172]Connor Pryor, Charles Dickens, Eriq Augustine, Alon Albalak, William Yang Wang, Lise Getoor:
NeuPSL: Neural Probabilistic Soft Logic. IJCAI 2023: 4145-4153 - [i40]Kaiwen Zhou, Kaizhi Zheng, Connor Pryor, Yilin Shen, Hongxia Jin, Lise Getoor, Xin Eric Wang:
ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation. CoRR abs/2301.13166 (2023) - 2022
- [j64]Sriram Srinivasan, Charles Dickens, Eriq Augustine, Golnoosh Farnadi, Lise Getoor:
A taxonomy of weight learning methods for statistical relational learning. Mach. Learn. 111(8): 2799-2838 (2022) - [c171]Alon Albalak, Varun Embar, Yi-Lin Tuan, Lise Getoor, William Yang Wang:
D-REX: Dialogue Relation Extraction with Explanations. ConvAI@ACL 2022: 34-46 - [c170]Hossam Sharara, Lise Getoor:
Multi-relational Affinity Propagation. ASONAM 2022: 117-124 - [c169]Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi, Connor Pryor, Luke Yoffe, Deepak Ramachandran, Lise Getoor, Jay Pujara, William Yang Wang:
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue. EMNLP 2022: 10936-10953 - [c168]Lise Getoor:
The Power of (Statistical) Relational Thinking. KDD 2022: 1 - [c167]Eriq Augustine, Connor Pryor, Charles Dickens, Jay Pujara, William Yang Wang, Lise Getoor:
Visual Sudoku Puzzle Classification: A Suite of Collective Neuro-Symbolic Tasks. NeSy 2022: 15-29 - [c166]Varun Embar, Sriram Srinivasan, Lise Getoor:
Learning explainable templated graphical models. UAI 2022: 621-630 - [i39]Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi, Connor Pryor, Luke Yoffe, Deepak Ramachandran, Lise Getoor, Jay Pujara, William Yang Wang:
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue. CoRR abs/2205.06262 (2022) - [i38]Connor Pryor, Charles Dickens, Eriq Augustine, Alon Albalak, William Yang Wang, Lise Getoor:
NeuPSL: Neural Probabilistic Soft Logic. CoRR abs/2205.14268 (2022) - [i37]Eriq Augustine, Pegah Jandaghi, Alon Albalak, Connor Pryor, Charles Dickens, William Yang Wang, Lise Getoor:
Emotion Recognition in Conversation using Probabilistic Soft Logic. CoRR abs/2207.07238 (2022) - [i36]Yi-Lin Tuan, Alon Albalak, Wenda Xu, Michael Saxon, Connor Pryor, Lise Getoor, William Yang Wang:
CausalDialogue: Modeling Utterance-level Causality in Conversations. CoRR abs/2212.10515 (2022) - 2021
- [j63]Varun Embar, Sriram Srinivasan, Lise Getoor:
A comparison of statistical relational learning and graph neural networks for aggregate graph queries. Mach. Learn. 110(7): 1847-1866 (2021) - [j62]Shawn Bailey, Yue Zhang, Arti Ramesh, Jennifer Golbeck, Lise Getoor:
A Structured and Linguistic Approach to Understanding Recovery and Relapse in AA. ACM Trans. Web 15(1): 5:1-5:35 (2021) - [c165]Varun Embar, Andrey Kan, Bunyamin Sisman, Christos Faloutsos, Lise Getoor:
DiffXtract: Joint Discriminative Product Attribute-Value Extraction. ICBK 2021: 271-280 - [c164]Charles Dickens, Connor Pryor, Eriq Augustine, Alexander Miller, Lise Getoor:
Context-Aware Online Collective Inference for Templated Graphical Models. ICML 2021: 2707-2716 - [c163]Yi-Lin Tuan, Connor Pryor, Wenhu Chen, Lise Getoor, William Yang Wang:
Local Explanation of Dialogue Response Generation. NeurIPS 2021: 404-416 - [i35]Yi-Lin Tuan, Connor Pryor, Wenhu Chen, Lise Getoor, William Yang Wang:
Local Explanation of Dialogue Response Generation. CoRR abs/2106.06528 (2021) - [i34]Alon Albalak, Varun Embar, Yi-Lin Tuan, Lise Getoor, William Yang Wang:
D-REX: Dialogue Relation Extraction with Explanations. CoRR abs/2109.05126 (2021) - 2020
- [j61]Lise Getoor:
Technical Perspective: Database Repair Meets Algorithmic Fairness. SIGMOD Rec. 49(1): 33 (2020) - [j60]Pigi Kouki, James Schaffer, Jay Pujara, John O'Donovan, Lise Getoor:
Generating and Understanding Personalized Explanations in Hybrid Recommender Systems. ACM Trans. Interact. Intell. Syst. 10(4): 31:1-31:40 (2020) - [j59]Arti Ramesh, Dan Goldwasser, Bert Huang, Hal Daumé III, Lise Getoor:
Interpretable Engagement Models for MOOCs Using Hinge-Loss Markov Random Fields. IEEE Trans. Learn. Technol. 13(1): 107-122 (2020) - [c162]Sriram Srinivasan, Eriq Augustine, Lise Getoor:
Tandem Inference: An Out-of-Core Streaming Algorithm for Very Large-Scale Relational Inference. AAAI 2020: 10259-10266 - [c161]Sriram Srinivasan, Golnoosh Farnadi, Lise Getoor:
BOWL: Bayesian Optimization for Weight Learning in Probabilistic Soft Logic. AAAI 2020: 10267-10275 - [c160]Varun Embar, Bunyamin Sisman, Hao Wei, Xin Luna Dong, Christos Faloutsos, Lise Getoor:
Contrastive Entity Linkage: Mining Variational Attributes from Large Catalogs for Entity Linkage. AKBC 2020 - [c159]Rajdipa Chowdhury, Sriram Srinivasan, Lise Getoor:
Joint Estimation of User And Publisher Credibility for Fake News Detection. CIKM 2020: 1993-1996 - [c158]Aaron Rodden, Tarun Salh, Eriq Augustine, Lise Getoor:
VMI-PSL: Visual Model Inspector for Probabilistic Soft Logic. RecSys 2020: 604-606 - [c157]Babak Salimi, Harsh Parikh, Moe Kayali, Lise Getoor, Sudeepa Roy, Dan Suciu:
Causal Relational Learning. SIGMOD Conference 2020: 241-256 - [i33]Golnoosh Farnadi, Lise Getoor, Marie-Francine Moens, Martine De Cock:
User Profiling Using Hinge-loss Markov Random Fields. CoRR abs/2001.01177 (2020) - [i32]Varun Embar, Sriram Srinivasan, Lise Getoor:
Estimating Aggregate Properties In Relational Networks With Unobserved Data. CoRR abs/2001.05617 (2020) - [i31]Babak Salimi, Harsh Parikh, Moe Kayali, Sudeepa Roy, Lise Getoor, Dan Suciu:
Causal Relational Learning. CoRR abs/2004.03644 (2020) - [i30]Charles Dickens, Rishika Singh, Lise Getoor:
HyperFair: A Soft Approach to Integrating Fairness Criteria. CoRR abs/2009.08952 (2020)
2010 – 2019
- 2019
- [j58]Golnoosh Farnadi, Behrouz Babaki, Lise Getoor:
A Declarative Approach to Fairness in Relational Domains. IEEE Data Eng. Bull. 42(3): 36-48 (2019) - [j57]Pigi Kouki, Jay Pujara, Christopher Marcum, Laura M. Koehly, Lise Getoor:
Collective entity resolution in multi-relational familial networks. Knowl. Inf. Syst. 61(3): 1547-1581 (2019) - [j56]Angelika Kimmig, Alex Memory, Renée J. Miller, Lise Getoor:
A Collective, Probabilistic Approach to Schema Mapping Using Diverse Noisy Evidence. IEEE Trans. Knowl. Data Eng. 31(8): 1426-1439 (2019) - [c156]Sriram Srinivasan, Behrouz Babaki, Golnoosh Farnadi, Lise Getoor:
Lifted Hinge-Loss Markov Random Fields. AAAI 2019: 7975-7983 - [c155]Lise Getoor:
Responsible Data Science. IEEE BigData 2019: 1 - [c154]Sriram Srinivasan, Nikhil S. Rao, Karthik Subbian, Lise Getoor:
Identifying Facet Mismatches In Search Via Micrographs. CIKM 2019: 1663-1672 - [c153]Lise Getoor:
The Power of Relational Learning (Invited Talk). ICDT 2019: 2:1-2:1 - [c152]Dhanya Sridhar, Lise Getoor:
Estimating Causal Effects of Tone in Online Debates. IJCAI 2019: 1872-1878 - [c151]Pigi Kouki, James Schaffer, Jay Pujara, John O'Donovan, Lise Getoor:
Personalized explanations for hybrid recommender systems. IUI 2019: 379-390 - [c150]Lise Getoor:
Responsible Data Science. SIGMOD Conference 2019: 1 - [c149]H. V. Jagadish, Francesco Bonchi, Tina Eliassi-Rad, Lise Getoor, Krishna P. Gummadi, Julia Stoyanovich:
The Responsibility Challenge for Data. SIGMOD Conference 2019: 412-414 - [c148]Lise Getoor:
Responsible Data Science. WWW (Companion Volume) 2019: 1265 - [i29]Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Randall Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i28]Dhanya Sridhar, Lise Getoor:
Estimating Causal Effects of Tone in Online Debates. CoRR abs/1906.04177 (2019) - 2018
- [c147]Golnoosh Farnadi, Behrouz Babaki, Lise Getoor:
Fairness-Aware Relational Learning and Inference. AAAI Workshops 2018: 333-335 - [c146]Golnoosh Farnadi, Behrouz Babaki, Lise Getoor:
Fairness in Relational Domains. AIES 2018: 108-114 - [c145]Sabina Tomkins, Lise Getoor, Yunfei Chen, Yi Zhang:
A Socio-linguistic Model for Cyberbullying Detection. ASONAM 2018: 53-60 - [c144]Sabina Tomkins, Golnoosh Farnadi, Brian Amanatullah, Lise Getoor, Steven Minton:
The Impact of Environmental Stressors on Human Trafficking. ICDM 2018: 507-516 - [c143]Dhanya Sridhar, Jay Pujara, Lise Getoor:
Scalable Probabilistic Causal Structure Discovery. IJCAI 2018: 5112-5118 - [c142]Lise Getoor:
Scalable structured prediction for richly structured socio-behavioral data. RecSys 2018: 2 - [c141]Sabina Tomkins, Steven Isley, Ben London, Lise Getoor:
Sustainability at scale: towards bridging the intention-behavior gap with sustainable recommendations. RecSys 2018: 214-218 - [c140]Arti Ramesh, Lise Getoor:
Topic Evolution Models for Long-Running MOOCs. WISE (2) 2018: 410-421 - [c139]Yue Zhang, Arti Ramesh, Jennifer Golbeck, Dhanya Sridhar, Lise Getoor:
A Structured Approach to Understanding Recovery and Relapse in AA. WWW 2018: 1205-1214 - [i27]Varun Embar, Dhanya Sridhar, Golnoosh Farnadi, Lise Getoor:
Scalable Structure Learning for Probabilistic Soft Logic. CoRR abs/1807.00973 (2018) - [i26]Golnoosh Farnadi, Pigi Kouki, Spencer K. Thompson, Sriram Srinivasan, Lise Getoor:
A Fairness-aware Hybrid Recommender System. CoRR abs/1809.09030 (2018) - 2017
- [j55]Stephen H. Bach, Matthias Broecheler, Bert Huang, Lise Getoor:
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic. J. Mach. Learn. Res. 18: 109:1-109:67 (2017) - [j54]Golnoosh Farnadi, Stephen H. Bach, Marie-Francine Moens, Lise Getoor, Martine De Cock:
Soft quantification in statistical relational learning. Mach. Learn. 106(12): 1971-1991 (2017) - [j53]Theodoros Rekatsinas, Saurav Ghosh, Sumiko R. Mekaru, Elaine O. Nsoesie, John S. Brownstein, Lise Getoor, Naren Ramakrishnan:
Forecasting rare disease outbreaks from open source indicators. Stat. Anal. Data Min. 10(2): 136-150 (2017) - [c138]Dhanya Sridhar, Jay Pujara, Lise Getoor:
Using Noisy Extractions to Discover Causal Knowledge. AKBC@NIPS 2017 - [c137]Jay Pujara, Eriq Augustine, Lise Getoor:
Sparsity and Noise: Where Knowledge Graph Embeddings Fall Short. EMNLP 2017: 1751-1756 - [c136]Angelika Kimmig, Alex Memory, Renée J. Miller, Lise Getoor:
A Collective, Probabilistic Approach to Schema Mapping. ICDE 2017: 921-932 - [c135]Pigi Kouki, Jay Pujara, Christopher Marcum, Laura M. Koehly, Lise Getoor:
Collective Entity Resolution in Familial Networks. ICDM 2017: 227-236 - [c134]Sabina Tomkins, Jay Pujara, Lise Getoor:
Disambiguating Energy Disaggregation: A Collective Probabilistic Approach. IJCAI 2017: 2857-2863 - [c133]Lise Getoor:
Statistical Relational Learning: Unifying AI & DB Perspectives on Structured Probabilistic Models. PODS 2017: 183 - [c132]Pigi Kouki, James Schaffer, Jay Pujara, John O'Donovan, Lise Getoor:
User Preferences for Hybrid Explanations. RecSys 2017: 84-88 - [c131]Arti Ramesh, Mario Rodriguez, Lise Getoor:
Multi-relational influence models for online professional networks. WI 2017: 291-298 - [c130]Sungchul Kim, Nikhil Kini, Jay Pujara, Eunyee Koh, Lise Getoor:
Probabilistic Visitor Stitching on Cross-Device Web Logs. WWW 2017: 1581-1589 - [r10]Galileo Namata, Prithviraj Sen, Mustafa Bilgic, Lise Getoor:
Collective Classification. Encyclopedia of Machine Learning and Data Mining 2017: 238-242 - [r9]Indrajit Bhattacharya, Lise Getoor:
Entity Resolution. Encyclopedia of Machine Learning and Data Mining 2017: 402-408 - [r8]Hossam Sharara, Lise Getoor:
Group Detection. Encyclopedia of Machine Learning and Data Mining 2017: 603-607 - [r7]Lise Getoor:
Link Mining and Link Discovery. Encyclopedia of Machine Learning and Data Mining 2017: 751-753 - [r6]Galileo Namata, Lise Getoor:
Link Prediction. Encyclopedia of Machine Learning and Data Mining 2017: 753-758 - [i25]Angelika Kimmig, Alex Memory, Renée J. Miller, Lise Getoor:
A Collective, Probabilistic Approach to Schema Mapping: Appendix. CoRR abs/1702.03447 (2017) - [i24]Dhanya Sridhar, Jay Pujara, Lise Getoor:
Using Noisy Extractions to Discover Causal Knowledge. CoRR abs/1711.05900 (2017) - 2016
- [j52]Sathappan Muthiah, Bert Huang, Jaime Arredondo, David Mares, Lise Getoor, Graham Katz, Naren Ramakrishnan:
Capturing Planned Protests from Open Source Indicators. AI Mag. 37(2): 63-75 (2016) - [j51]Dhanya Sridhar, Shobeir Fakhraei, Lise Getoor:
A probabilistic approach for collective similarity-based drug-drug interaction prediction. Bioinform. 32(20): 3175-3182 (2016) - [j50]Ben London, Bert Huang, Lise Getoor:
Stability and Generalization in Structured Prediction. J. Mach. Learn. Res. 17: 222:1-222:52 (2016) - [j49]Galileo Mark S. Namata Jr., Ben London, Lise Getoor:
Collective Graph Identification. ACM Trans. Knowl. Discov. Data 10(3): 25:1-25:36 (2016) - [c129]Shachi H. Kumar, Jay Pujara, Lise Getoor, David Mares, Dipak Gupta, Ellen Riloff:
Unsupervised models for predicting strategic relations between organizations. ASONAM 2016: 711-718 - [c128]Brian Uzzi, Lise Getoor, Evimaria Terzi, Lada A. Adamic:
ASONAM 2016 keynotes: Ideas and inventions. ASONAM 2016: xl-xliii - [c127]V. S. Subrahmanian, Lada A. Adamic, Lise Getoor, Evimaria Terzi, Brian Uzzi, Lisa Singh:
ASONAM 2016 panel: Social network analysis for social good. ASONAM 2016: xlvii - [c126]Sabina Tomkins, Arti Ramesh, Lise Getoor:
Predicting Post-Test Performance from Student Behavior: A High School MOOC Case Study. EDM 2016: 239-246 - [c125]Theodoros Rekatsinas, Amol Deshpande, Xin Luna Dong, Lise Getoor, Divesh Srivastava:
SourceSight: Enabling Effective Source Selection. SIGMOD Conference 2016: 2157-2160 - [i23]Shobeir Fakhraei, Dhanya Sridhar, Jay Pujara, Lise Getoor:
Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks. CoRR abs/1607.00474 (2016) - [i22]Jay Pujara, Lise Getoor:
Generic Statistical Relational Entity Resolution in Knowledge Graphs. CoRR abs/1607.00992 (2016) - 2015
- [j48]Jay Pujara, Hui Miao, Lise Getoor, William W. Cohen:
Using Semantics and Statistics to Turn Data into Knowledge. AI Mag. 36(1): 65-74 (2015) - [j47]Naren Ramakrishnan, Chang-Tien Lu, Madhav V. Marathe, Achla Marathe, Anil Vullikanti, Stephen G. Eubank, Scotland Leman, Michael J. Roan, John S. Brownstein, Kristen Maria Summers, Lise Getoor, Aravind Srinivasan, Tanzeem Choudhury, Dipak Gupta, David Mares:
Model-Based Forecasting of Significant Societal Events. IEEE Intell. Syst. 30(5): 86-90 (2015) - [j46]Angelika Kimmig, Lilyana Mihalkova, Lise Getoor:
Lifted graphical models: a survey. Mach. Learn. 99(1): 1-45 (2015) - [c124]Sathappan Muthiah, Bert Huang, Jaime Arredondo, David Mares, Lise Getoor, Graham Katz, Naren Ramakrishnan:
Planned Protest Modeling in News and Social Media. AAAI 2015: 3920-3927 - [c123]Arti Ramesh, Shachi H. Kumar, James R. Foulds, Lise Getoor:
Weakly Supervised Models of Aspect-Sentiment for Online Course Discussion Forums. ACL (1) 2015: 74-83 - [c122]Dhanya Sridhar, James R. Foulds, Bert Huang, Lise Getoor, Marilyn A. Walker:
Joint Models of Disagreement and Stance in Online Debate. ACL (1) 2015: 116-125 - [c121]Stephen H. Bach, Bert Huang, Lise Getoor:
Unifying Local Consistency and MAX SAT Relaxations for Scalable Inference with Rounding Guarantees. AISTATS 2015 - [c120]Theodoros Rekatsinas, Xin Luna Dong, Lise Getoor, Divesh Srivastava:
Finding Quality in Quantity: The Challenge of Discovering Valuable Sources for Integration. CIDR 2015 - [c119]Adam Grycner, Gerhard Weikum, Jay Pujara, James R. Foulds, Lise Getoor:
RELLY: Inferring Hypernym Relationships Between Relational Phrases. EMNLP 2015: 971-981 - [c118]Stephen H. Bach, Bert Huang, Jordan L. Boyd-Graber, Lise Getoor:
Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs. ICML 2015: 381-390 - [c117]Ben London, Bert Huang, Lise Getoor:
The Benefits of Learning with Strongly Convex Approximate Inference. ICML 2015: 410-418 - [c116]James R. Foulds, Shachi H. Kumar, Lise Getoor:
Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models. ICML 2015: 777-786 - [c115]Xinran He, Theodoros Rekatsinas, James R. Foulds, Lise Getoor, Yan Liu:
HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades. ICML 2015: 871-880 - [c114]Golnoosh Farnadi, Stephen H. Bach, Marjon Blondeel, Marie-Francine Moens, Lise Getoor, Martine De Cock:
Statistical Relational Learning with Soft Quantifiers. ILP 2015: 60-75 - [c113]Shobeir Fakhraei, James R. Foulds, Madhusudana V. S. Shashanka, Lise Getoor:
Collective Spammer Detection in Evolving Multi-Relational Social Networks. KDD 2015: 1769-1778 - [c112]Pigi Kouki, Shobeir Fakhraei, James R. Foulds, Magdalini Eirinaki, Lise Getoor:
HyPER: A Flexible and Extensible Probabilistic Framework for Hybrid Recommender Systems. RecSys 2015: 99-106 - [c111]Theodoros Rekatsinas, Saurav Ghosh, Sumiko R. Mekaru, Elaine O. Nsoesie, John S. Brownstein, Lise Getoor, Naren Ramakrishnan:
SourceSeer: Forecasting Rare Disease Outbreaks Using Multiple Data Sources. SDM 2015: 379-387 - [c110]Sabina Tomkins, Lise Getoor:
Poster Abstract: Contextual Air Conditioning Disaggregation with Probabilistic Soft Logic. BuildSys 2015: 117-118 - [c109]Jay Pujara, Ben London, Lise Getoor:
Budgeted Online Collective Inference. UAI 2015: 712-721 - [p4]Shobeir Fakhraei, Eberechukwu Onukwugha, Lise Getoor:
Data Analytics for Pharmaceutical Discoveries. Healthcare Data Analytics 2015: 599-623 - [i21]Stephen H. Bach, Matthias Broecheler, Bert Huang, Lise Getoor:
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic. CoRR abs/1505.04406 (2015) - 2014
- [j45]Shobeir Fakhraei, Bert Huang, Louiqa Raschid, Lise Getoor:
Network-Based Drug-Target Interaction Prediction with Probabilistic Soft Logic. IEEE ACM Trans. Comput. Biol. Bioinform. 11(5): 775-787 (2014) - [j44]Bradley Skaggs, Lise Getoor:
Topic Modeling for Wikipedia Link Disambiguation. ACM Trans. Inf. Syst. 32(3): 10:1-10:24 (2014) - [c108]Golnoosh Farnadi, Stephen H. Bach, Marie-Francine Moens, Lise Getoor, Martine De Cock:
Extending PSL with Fuzzy Quantifiers. StarAI@AAAI 2014 - [c107]Arti Ramesh, Dan Goldwasser, Bert Huang, Hal Daumé III, Lise Getoor:
Learning Latent Engagement Patterns of Students in Online Courses. AAAI 2014: 1272-1278 - [c106]Ben London, Bert Huang, Ben Taskar, Lise Getoor:
PAC-Bayesian Collective Stability. AISTATS 2014: 585-594 - [c105]Arti Ramesh, Dan Goldwasser, Bert Huang, Hal Daumé III, Lise Getoor:
Understanding MOOC Discussion Forums using Seeded LDA. BEA@ACL 2014: 28-33 - [c104]Aravindan Mahendiran, Wei Wang, Jaime Arredondo Sanchez Lira, Bert Huang, Lise Getoor, David Mares, Naren Ramakrishnan:
Discovering evolving political vocabulary in social media. BESC 2014: 26-32 - [c103]Walaa Eldin Moustafa, Angelika Kimmig, Amol Deshpande, Lise Getoor:
Subgraph pattern matching over uncertain graphs with identity linkage uncertainty. ICDE 2014: 904-915 - [c102]Naren Ramakrishnan, Patrick Butler, Sathappan Muthiah, Nathan Self, Rupinder Paul Khandpur, Parang Saraf, Wei Wang, Jose Cadena, Anil Vullikanti, Gizem Korkmaz, Chris J. Kuhlman, Achla Marathe, Liang Zhao, Ting Hua, Feng Chen, Chang-Tien Lu, Bert Huang, Aravind Srinivasan, Khoa Trinh, Lise Getoor, Graham Katz, Andy Doyle, Chris Ackermann, Ilya Zavorin, Jim Ford, Kristen Maria Summers, Youssef Fayed, Jaime Arredondo, Dipak Gupta, David Mares:
'Beating the news' with EMBERS: forecasting civil unrest using open source indicators. KDD 2014: 1799-1808 - [c101]Arti Ramesh, Dan Goldwasser, Bert Huang, Hal Daumé III, Lise Getoor:
Uncovering hidden engagement patterns for predicting learner performance in MOOCs. L@S 2014: 157-158 - [p3]Ben London, Lise Getoor:
Collective Classification of Network Data. Data Classification: Algorithms and Applications 2014: 399-416 - [i20]Mustafa Bilgic, Lise Getoor:
Value of Information Lattice: Exploiting Probabilistic Independence for Effective Feature Subset Acquisition. CoRR abs/1401.3881 (2014) - [i19]Naren Ramakrishnan, Patrick Butler, Sathappan Muthiah, Nathan Self, Rupinder Paul Khandpur, Parang Saraf, Wei Wang, Jose Cadena, Anil Vullikanti, Gizem Korkmaz, Chris J. Kuhlman, Achla Marathe, Liang Zhao, Ting Hua, Feng Chen, Chang-Tien Lu, Bert Huang, Aravind Srinivasan, Khoa Trinh, Lise Getoor, Graham Katz, Andy Doyle, Chris Ackermann, Ilya Zavorin, Jim Ford, Kristen Maria Summers, Youssef Fayed, Jaime Arredondo, Dipak Gupta, David Mares:
'Beating the news' with EMBERS: Forecasting Civil Unrest using Open Source Indicators. CoRR abs/1402.7035 (2014) - 2013
- [j43]Panagiotis Papadimitriou, Panayiotis Tsaparas, Ariel Fuxman, Lise Getoor:
TACI: Taxonomy-Aware Catalog Integration. IEEE Trans. Knowl. Data Eng. 25(7): 1643-1655 (2013) - [c100]Jay Pujara, Hui Miao, Lise Getoor, William W. Cohen:
Large-Scale Knowledge Graph Identification Using PSL. AAAI Fall Symposia 2013 - [c99]Hui Miao, Xiangyang Liu, Bert Huang, Lise Getoor:
A hypergraph-partitioned vertex programming approach for large-scale consensus optimization. IEEE BigData 2013: 563-568 - [c98]Jay Pujara, Hui Miao, Lise Getoor, William W. Cohen:
Ontology-aware partitioning for knowledge graph identification. AKBC@CIKM 2013: 19-24 - [c97]Ben London, Sameh Khamis, Stephen H. Bach, Bert Huang, Lise Getoor, Larry S. Davis:
Collective Activity Detection Using Hinge-loss Markov Random Fields. CVPR Workshops 2013: 566-571 - [c96]Ben London, Bert Huang, Ben Taskar, Lise Getoor:
Collective Stability in Structured Prediction: Generalization from One Example. ICML (3) 2013: 828-836 - [c95]Shobeir Fakhraei, Louiqa Raschid, Lise Getoor:
Drug-target interaction prediction for drug repurposing with probabilistic similarity logic. BIOKDD 2013: 10-17 - [c94]Yaojia Zhu, Xiaoran Yan, Lise Getoor, Cristopher Moore:
Scalable text and link analysis with mixed-topic link models. KDD 2013: 473-481 - [c93]Lise Getoor, Ashwin Machanavajjhala:
Entity resolution for big data. KDD 2013: 1527 - [c92]Lise Getoor, Ashwin Machanavajjhala:
Network sampling. KDD 2013: 1528 - [c91]Lise Getoor:
Probabilistic Soft Logic: A Scalable Approach for Markov Random Fields over Continuous-Valued Variables - (Abstract of Keynote Talk). RuleML 2013: 1 - [c90]Jeon-Hyung Kang, Kristina Lerman, Lise Getoor:
LA-LDA: A Limited Attention Topic Model for Social Recommendation. SBP 2013: 211-220 - [c89]Bert Huang, Angelika Kimmig, Lise Getoor, Jennifer Golbeck:
A Flexible Framework for Probabilistic Models of Social Trust. SBP 2013: 265-273 - [c88]Jay Pujara, Hui Miao, Lise Getoor, William W. Cohen:
Knowledge Graph Identification. ISWC (1) 2013: 542-557 - [c87]Walaa Eldin Moustafa, Hui Miao, Amol Deshpande, Lise Getoor:
GRDB: a system for declarative and interactive analysis of noisy information networks. SIGMOD Conference 2013: 1085-1088 - [c86]Stephen H. Bach, Bert Huang, Ben London, Lise Getoor:
Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction. UAI 2013 - [i18]Jeon-Hyung Kang, Kristina Lerman, Lise Getoor:
LA-LDA: A Limited Attention Topic Model for Social Recommendation. CoRR abs/1301.6277 (2013) - [i17]Urszula Chajewska, Lise Getoor, Joseph Norman, Yuval Shahar:
Utility Elicitation as a Classification Problem. CoRR abs/1301.7367 (2013) - [i16]Ben London, Bert Huang, Lise Getoor:
Graph-based Generalization Bounds for Learning Binary Relations. CoRR abs/1302.5348 (2013) - [i15]Ben London, Theodoros Rekatsinas, Bert Huang, Lise Getoor:
Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss. CoRR abs/1303.1733 (2013) - [i14]Yaojia Zhu, Xiaoran Yan, Lise Getoor, Cristopher Moore:
Scalable Text and Link Analysis with Mixed-Topic Link Models. CoRR abs/1303.7264 (2013) - [i13]Walaa Eldin Moustafa, Angelika Kimmig, Amol Deshpande, Lise Getoor:
Subgraph Pattern Matching over Uncertain Graphs with Identity Linkage Uncertainty. CoRR abs/1305.7006 (2013) - [i12]Hui Miao, Xiangyang Liu, Bert Huang, Lise Getoor:
A Hypergraph-Partitioned Vertex Programming Approach for Large-scale Consensus Optimization. CoRR abs/1308.6823 (2013) - [i11]Stephen H. Bach, Bert Huang, Ben London, Lise Getoor:
Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction. CoRR abs/1309.6813 (2013) - 2012
- [b2]Elena Zheleva, Evimaria Terzi, Lise Getoor:
Privacy in Social Networks. Synthesis Lectures on Data Mining and Knowledge Discovery, Morgan & Claypool Publishers 2012, ISBN 978-3-031-00773-6 - [j42]Lise Getoor, Ashwin Machanavajjhala:
Entity Resolution: Theory, Practice & Open Challenges. Proc. VLDB Endow. 5(12): 2018-2019 (2012) - [j41]Heasoo Hwang, Hady Wirawan Lauw, Lise Getoor, Alexandros Ntoulas:
Organizing User Search Histories. IEEE Trans. Knowl. Data Eng. 24(5): 912-925 (2012) - [c85]Walaa Eldin Moustafa, Amol Deshpande, Lise Getoor:
Ego-centric Graph Pattern Census. ICDE 2012: 234-245 - [c84]Stephen H. Bach, Matthias Broecheler, Lise Getoor, Dianne P. O'Leary:
Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization. NIPS 2012: 2663-2671 - [c83]Alex Memory, Angelika Kimmig, Stephen H. Bach, Louiqa Raschid, Lise Getoor:
Graph Summarization in Annotated Data Using Probabilistic Soft Logic. URSW 2012: 75-86 - [c82]Theodoros Rekatsinas, Amol Deshpande, Lise Getoor:
Local structure and determinism in probabilistic databases. SIGMOD Conference 2012: 373-384 - [c81]Hossam Sharara, Lisa Singh, Lise Getoor, Janet Mann:
Stability vs. Diversity: Understanding the Dynamics of Actors in Time-Varying Affiliation Networks. SocialInformatics 2012: 1-6 - [c80]Bert Huang, Angelika Kimmig, Lise Getoor, Jennifer Golbeck:
Probabilistic Soft Logic for Trust Analysis in Social Networks. StarAI@UAI 2012 - [i10]Matthias Bröcheler, Lilyana Mihalkova, Lise Getoor:
Probabilistic Similarity Logic. CoRR abs/1203.3469 (2012) - [i9]Prithviraj Sen, Amol Deshpande, Lise Getoor:
Bisimulation-based Approximate Lifted Inference. CoRR abs/1205.2616 (2012) - 2011
- [j40]Mustafa Bilgic, Lise Getoor:
Value of Information Lattice: Exploiting Probabilistic Independence for Effective Feature Subset Acquisition. J. Artif. Intell. Res. 41: 69-95 (2011) - [j39]Louis Licamele, Lise Getoor:
A Method for the Detection of Meaningful and Reproducible Group Signatures from Gene Expression Profiles. J. Bioinform. Comput. Biol. 9(3): 431-451 (2011) - [j38]Daozheng Chen, Mustafa Bilgic, Lise Getoor, David W. Jacobs:
Dynamic Processing Allocation in Video. IEEE Trans. Pattern Anal. Mach. Intell. 33(11): 2174-2187 (2011) - [j37]Hossam Sharara, Lisa Singh, Lise Getoor, Janet Mann:
Understanding actor loyalty to event-based groups in affiliation networks. Soc. Netw. Anal. Min. 1(2): 115-126 (2011) - [c79]Jay Pujara, Hal Daumé III, Lise Getoor:
Using classifier cascades for scalable e-mail classification. CEAS 2011: 55-63 - [c78]Walaa Eldin Moustafa, Galileo Namata, Amol Deshpande, Lise Getoor:
Declarative analysis of noisy information networks. ICDE Workshops 2011: 106-111 - [c77]Steven Minton, Matthew Michelson, Kane See, Sofus A. Macskassy, Bora Gazen, Lise Getoor:
Improving Classifier Performance by Autonomously Collecting Background Knowledge from the Web. ICMLA (1) 2011: 1-6 - [c76]Hossam Sharara, William Rand, Lise Getoor:
Differential Adaptive Diffusion: Understanding Diversity and Learning whom to Trust in Viral Marketing. ICWSM 2011 - [c75]Hossam Sharara, Awalin Sopan, Galileo Namata, Lise Getoor, Lisa Singh:
G-PARE: A visual analytic tool for comparative analysis of uncertain graphs. IEEE VAST 2011: 61-70 - [c74]Hossam Sharara, Lise Getoor, Myra Norton:
Active Surveying: A Probabilistic Approach for Identifying Key Opinion Leaders. IJCAI 2011: 1485-1490 - [c73]Galileo Namata, Stanley Kok, Lise Getoor:
Collective graph identification. KDD 2011: 87-95 - [c72]Lise Getoor, Lilyana Mihalkova:
Learning statistical models from relational data. SIGMOD Conference 2011: 1195-1198 - [c71]Lise Getoor, Lilyana Mihalkova:
Exploiting statistical and relational information on the web and in social media. WSDM 2011: 9-10 - [c70]Matthew Michelson, Sofus A. Macskassy, Steven Minton, Lise Getoor:
Materializing multi-relational databases from the web using taxonomic queries. WSDM 2011: 355-364 - [c69]Anon Plangprasopchok, Kristina Lerman, Lise Getoor:
A probabilistic approach for learning folksonomies from structured data. WSDM 2011: 555-564 - [p2]Elena Zheleva, Lise Getoor:
Privacy in Social Networks: A Survey. Social Network Data Analytics 2011: 277-306 - [e6]Lise Getoor, Tobias Scheffer:
Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28 - July 2, 2011. Omnipress 2011 [contents] - [i8]Lilyana Mihalkova, Lise Getoor:
Lifted Graphical Models: A Survey. CoRR abs/1107.4966 (2011) - [i7]Indrajit Bhattacharya, Lise Getoor:
Query-time Entity Resolution. CoRR abs/1111.0045 (2011) - 2010
- [j36]Eric Horvitz, Lise Getoor, Carlos Guestrin, James A. Hendler, Joseph A. Konstan, Devika Subramanian, Michael P. Wellman, Henry A. Kautz:
AI Theory and Practice: A Discussion on Hard Challenges and Opportunities Ahead. AI Mag. 31(3): 103-114 (2010) - [j35]Louis Licamele, Lise Getoor:
Indirect two-sided relative ranking: a robust similarity measure for gene expression data. BMC Bioinform. 11: 137 (2010) - [j34]Prithviraj Sen, Amol Deshpande, Lise Getoor:
Read-Once Functions and Query Evaluation in Probabilistic Databases. Proc. VLDB Endow. 3(1): 1068-1079 (2010) - [j33]Galileo Mark S. Namata Jr., Lise Getoor:
Identifying graphs from noisy and incomplete data. SIGKDD Explor. 12(1): 33-39 (2010) - [j32]Ulf Brefeld, Lise Getoor, Sofus A. Macskassy:
Eighth workshop on mining and learning with graphs. SIGKDD Explor. 12(2): 63-65 (2010) - [c68]Mustafa Bilgic, Lise Getoor:
Active Inference for Collective Classification. AAAI 2010: 1652-1655 - [c67]Anon Plangprasopchok, Kristina Lerman, Lise Getoor:
Constructing Folksonomies by Integrating Structured Metadata with Relational Clustering. Collaboratively-Built Knowledge Sources and AI 2010 - [c66]Anon Plangprasopchok, Kristina Lerman, Lise Getoor:
Integrating Structured Metadata with Relational Affinity Propagation. StarAI@AAAI 2010 - [c65]Louis Licamele, Lise Getoor:
A Method for the Detection of Meaningful and Reproducible Group Signatures from Gene Expression Profiles. BIONETICS 2010: 387-401 - [c64]Mustafa Bilgic, Lilyana Mihalkova, Lise Getoor:
Active Learning for Networked Data. ICML 2010: 79-86 - [c63]Lise Getoor:
Graph Identification. IDA 2010: 6-7 - [c62]Anon Plangprasopchok, Kristina Lerman, Lise Getoor:
Growing a tree in the forest: constructing folksonomies by integrating structured metadata. KDD 2010: 949-958 - [c61]Matthias Broecheler, Lise Getoor:
Computing Marginal Distributions over Continuous Markov Networks for Statistical Relational Learning. NIPS 2010: 316-324 - [c60]Janardhan Rao Doppa, Jun Yu, Prasad Tadepalli, Lise Getoor:
Learning Algorithms for Link Prediction Based on Chance Constraints. ECML/PKDD (1) 2010: 344-360 - [c59]Matthias Bröcheler, Lilyana Mihalkova, Lise Getoor:
Probabilistic Similarity Logic. UAI 2010: 73-82 - [c58]Anon Plangprasopchok, Kristina Lerman, Lise Getoor:
Constructing folksonomies by integrating structured metadata. WWW 2010: 1165-1166 - [p1]Galileo Mark S. Namata Jr., Hossam Sharara, Lise Getoor:
A Survey of Link Mining Tasks for Analyzing Noisy and Incomplete Networks. Link Mining 2010: 107-133 - [e5]Ulf Brefeld, Lise Getoor, Sofus A. Macskassy:
Proceedings of the Eighth Workshop on Mining and Learning with Graphs, MLG '10, Washington, D.C., USA, July 24-25, 2010. ACM 2010, ISBN 978-1-4503-0214-2 [contents] - [r5]Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor:
Collective Classification. Encyclopedia of Machine Learning 2010: 189-193 - [r4]Indrajit Bhattacharya, Lise Getoor:
Entity Resolution. Encyclopedia of Machine Learning 2010: 321-326 - [r3]Hossam Sharara, Lise Getoor:
Group Detection. Encyclopedia of Machine Learning 2010: 489-492 - [r2]Lise Getoor:
Link Mining and Link Discovery. Encyclopedia of Machine Learning 2010: 606-609 - [r1]Galileo Namata, Lise Getoor:
Link Prediction. Encyclopedia of Machine Learning 2010: 609-612 - [i6]Anon Plangprasopchok, Kristina Lerman, Lise Getoor:
Integrating Structured Metadata with Relational Affinity Propagation. CoRR abs/1005.4963 (2010) - [i5]Anon Plangprasopchok, Kristina Lerman, Lise Getoor:
Growing a Tree in the Forest: Constructing Folksonomies by Integrating Structured Metadata. CoRR abs/1005.5114 (2010) - [i4]Anon Plangprasopchok, Kristina Lerman, Lise Getoor:
A Probabilistic Approach for Learning Folksonomies from Structured Data. CoRR abs/1011.3557 (2010)
2000 – 2009
- 2009
- [j31]Karl Schnaitter, Neoklis Polyzotis, Lise Getoor:
Index Interactions in Physical Design Tuning: Modeling, Analysis, and Applications. Proc. VLDB Endow. 2(1): 1234-1245 (2009) - [j30]Jian Pei, Lise Getoor, Ander de Keijzer:
Summary of the first ACM SIGKDD workshop on knowledge discovery from uncertain data (U'09). SIGKDD Explor. 11(2): 90-91 (2009) - [j29]Mustafa Bilgic, Lise Getoor:
Reflect and correct: A misclassification prediction approach to active inference. ACM Trans. Knowl. Discov. Data 3(4): 20:1-20:32 (2009) - [j28]Prithviraj Sen, Amol Deshpande, Lise Getoor:
PrDB: managing and exploiting rich correlations in probabilistic databases. VLDB J. 18(5): 1065-1090 (2009) - [c57]Hossam Sharara, Lisa Singh, Lise Getoor, Janet Mann:
The Dynamics of Actor Loyalty to Groups in Affiliation Networks. ASONAM 2009: 101-106 - [c56]Swapna Somasundaran, Galileo Namata, Janyce Wiebe, Lise Getoor:
Supervised and Unsupervised Methods in Employing Discourse Relations for Improving Opinion Polarity Classification. EMNLP 2009: 170-179 - [c55]Vladimir Barash, Marc A. Smith, Lise Getoor, Howard T. Welser:
Distinguishing Knowledge vs Social Capital in Social Media with Roles and Context. ICWSM 2009 - [c54]Galileo Mark S. Namata Jr., Lise Getoor:
Identifying graphs from noisy and incomplete data. KDD Workshop on Knowledge Discovery from Uncertain Data 2009: 23-29 - [c53]Elena Zheleva, Hossam Sharara, Lise Getoor:
Co-evolution of social and affiliation networks. KDD 2009: 1007-1016 - [c52]Hassan Sayyadi, Lise Getoor:
FutureRank: Ranking Scientific Articles by Predicting their Future PageRank. SDM 2009: 533-544 - [c51]Barna Saha, Lise Getoor:
On Maximum Coverage in the Streaming Model & Application to Multi-topic Blog-Watch. SDM 2009: 697-708 - [c50]Swapna Somasundaran, Galileo Namata, Lise Getoor, Janyce Wiebe:
Opinion Graphs for Polarity and Discourse Classification. Graph-based Methods for Natural Language Processing 2009: 66-74 - [c49]Prithviraj Sen, Amol Deshpande, Lise Getoor:
Bisimulation-based Approximate Lifted Inference. UAI 2009: 496-505 - [c48]Elena Zheleva, Lise Getoor:
To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles. WWW 2009: 531-540 - [e4]Jian Pei, Lise Getoor, Ander de Keijzer:
Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data, Paris, France, June 28, 2009. ACM 2009, ISBN 978-1-60558-675-5 [contents] - 2008
- [j27]Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, Tina Eliassi-Rad:
Collective Classification in Network Data. AI Mag. 29(3): 93-106 (2008) - [j26]Manfred Jaeger, Lise Getoor, Kristian Kersting:
Preface. Ann. Math. Artif. Intell. 54(1-3): 1-2 (2008) - [j25]Marie desJardins, Priyang Rathod, Lise Getoor:
Learning Structured Bayesian Networks: Combining Abstraction Hierarchies and Tree-Structured Conditional Probability Tables. Comput. Intell. 24(1): 1-22 (2008) - [j24]Prithviraj Sen, Lise Getoor:
Cost-sensitive learning with conditional Markov networks. Data Min. Knowl. Discov. 17(2): 136-163 (2008) - [j23]Thomas G. Dietterich, Pedro M. Domingos, Lise Getoor, Stephen H. Muggleton, Prasad Tadepalli:
Structured machine learning: the next ten years. Mach. Learn. 73(1): 3-23 (2008) - [j22]Prithviraj Sen, Amol Deshpande, Lise Getoor:
Exploiting shared correlations in probabilistic databases. Proc. VLDB Endow. 1(1): 809-820 (2008) - [j21]Elena Zheleva, Aleksander Kolcz, Lise Getoor:
Trusting spam reporters: A reporter-based reputation system for email filtering. ACM Trans. Inf. Syst. 27(1): 3:1-3:27 (2008) - [j20]Hyunmo Kang, Lise Getoor, Ben Shneiderman, Mustafa Bilgic, Louis Licamele:
Interactive Entity Resolution in Relational Data: A Visual Analytic Tool and Its Evaluation. IEEE Trans. Vis. Comput. Graph. 14(5): 999-1014 (2008) - [c47]Marc A. Smith, Vladimir Barash, Lise Getoor, Hady Wirawan Lauw:
Leveraging social context for searching social media. SSM 2008: 91-94 - [c46]Mustafa Bilgic, Lise Getoor:
Effective label acquisition for collective classification. KDD 2008: 43-51 - [c45]Elena Zheleva, Lise Getoor, Jennifer Golbeck, Ugur Kuter:
Using Friendship Ties and Family Circles for Link Prediction. SNAKDD 2008: 97-113 - [e3]Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Kristian Kersting, Stephen H. Muggleton:
Probabilistic, Logical and Relational Learning - A Further Synthesis, 15.04. - 20.04.2007. Dagstuhl Seminar Proceedings 07161, Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany 2008 [contents] - 2007
- [j19]Rezarta Islamaj Dogan, Lise Getoor, W. John Wilbur, Stephen M. Mount:
Features generated for computational splice-site prediction correspond to functional elements. BMC Bioinform. 8 (2007) - [j18]Lisa Singh, Lise Getoor:
Increasing the Predictive Power of Affiliation Networks. IEEE Data Eng. Bull. 30(2): 41-50 (2007) - [j17]Indrajit Bhattacharya, Lise Getoor:
Query-time Entity Resolution. J. Artif. Intell. Res. 30: 621-657 (2007) - [j16]Rezarta Islamaj Dogan, Lise Getoor, W. John Wilbur, Stephen M. Mount:
SplicePort - An interactive splice-site analysis tool. Nucleic Acids Res. 35(Web-Server-Issue): 285-291 (2007) - [j15]Hyunmo Kang, Lise Getoor, Lisa Singh:
Visual analysis of dynamic group membership in temporal social networks. SIGKDD Explor. 9(2): 13-21 (2007) - [j14]Indrajit Bhattacharya, Lise Getoor:
Collective entity resolution in relational data. ACM Trans. Knowl. Discov. Data 1(1): 5 (2007) - [j13]Edward Hung, Lise Getoor, V. S. Subrahmanian:
Probabilistic interval XML. ACM Trans. Comput. Log. 8(4): 24 (2007) - [c44]Christopher P. Diehl, Galileo Namata, Lise Getoor:
Relationship Identification for Social Network Discovery. AAAI 2007: 546-552 - [c43]Mustafa Bilgic, Lise Getoor:
VOILA: Efficient Feature-value Acquisition for Classification. AAAI 2007: 1225-1230 - [c42]Indrajit Bhattacharya, Lise Getoor:
Online Collective Entity Resolution. AAAI 2007: 1606-1609 - [c41]Galileo Namata, Brian Staats, Lise Getoor, Ben Shneiderman:
A dual-view approach to interactive network visualization. CIKM 2007: 939-942 - [c40]Rezarta Islamaj Dogan, Lise Getoor, W. John Wilbur:
Characterizing RNA Secondary-Structure Features and Their Effects on Splice-Site Prediction. ICDM Workshops 2007: 89-94 - [c39]Mustafa Bilgic, Galileo Namata, Lise Getoor:
Combining Collective Classification and Link Prediction. ICDM Workshops 2007: 381-386 - [c38]Prithviraj Sen, Amol Deshpande, Lise Getoor:
Representing Tuple and Attribute Uncertainty in Probabilistic Databases. ICDM Workshops 2007: 507-512 - [c37]Hyunmo Kang, Lise Getoor, Lisa Singh:
C-GROUP: A Visual Analytic Tool for Pairwise Analysis of Dynamic Group Membership. IEEE VAST 2007: 211-212 - [c36]Hyunmo Kang, Vivek Sehgal, Lise Getoor:
GeoDDupe: A Novel Interface for Interactive Entity Resolution in Geospatial Data. IV 2007: 489-496 - [c35]Lisa Singh, Mitchell Beard, Lise Getoor, M. Brian Blake:
Visual Mining of Multi-Modal Social Networks at Different Abstraction Levels. IV 2007: 672-679 - [c34]Elena Zheleva, Lise Getoor:
Preserving the Privacy of Sensitive Relationships in Graph Data. PinKDD 2007: 153-171 - [c33]Lise Getoor:
Graph Identification. MLG 2007 - [c32]Lise Getoor:
Combining Tuple and Attribute Uncertainty in Probabilistic Databases. MUD 2007: 1-2 - [c31]Octavian Udrea, Lise Getoor, Renée J. Miller:
Leveraging data and structure in ontology integration. SIGMOD Conference 2007: 449-460 - [i3]Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Kristian Kersting, Stephen H. Muggleton:
07161 Abstracts Collection -- Probabilistic, Logical and Relational Learning - A Further Synthesis. Probabilistic, Logical and Relational Learning - A Further Synthesis 2007 - 2006
- [j12]Lise Getoor:
An Introduction to Probabilistic Graphical Models for Relational Data. IEEE Data Eng. Bull. 29(1): 32-39 (2006) - [j11]Indrajit Bhattacharya, Lise Getoor:
Collective Entity Resolution In Relational Data. IEEE Data Eng. Bull. 29(2): 4-12 (2006) - [j10]Lise Getoor, John Grant:
PRL: A probabilistic relational language. Mach. Learn. 62(1-2): 7-31 (2006) - [j9]Gregory Piatetsky-Shapiro, Chabane Djeraba, Lise Getoor, Robert Grossman, Ronen Feldman, Mohammed Javeed Zaki:
What are the grand challenges for data mining?: KDD-2006 panel report. SIGKDD Explor. 8(2): 70-77 (2006) - [c30]Vivek Sehgal, Lise Getoor, Peter Viechnicki:
Entity resolution in geospatial data integration. GIS 2006: 83-90 - [c29]Louis Licamele, Lise Getoor:
Social Capital in Friendship-Event Networks. ICDM 2006: 959-964 - [c28]Galileo Mark S. Namata Jr., Lise Getoor, Christopher P. Diehl:
Inferring Organizational Titles in Online Communication. SNA@ICML 2006: 179-181 - [c27]Prithviraj Sen, Lise Getoor:
Cost-sensitive learning with conditional Markov networks. ICML 2006: 801-808 - [c26]Mustafa Bilgic, Louis Licamele, Lise Getoor, Ben Shneiderman:
D-Dupe: An Interactive Tool for Entity Resolution in Social Networks. IEEE VAST 2006: 43-50 - [c25]Indrajit Bhattacharya, Lise Getoor, Louis Licamele:
Query-time entity resolution. KDD 2006: 529-534 - [c24]Gregory Piatetsky-Shapiro, Robert Grossman, Chabane Djeraba, Ronen Feldman, Lise Getoor, Mohammed Javeed Zaki:
Is there a grand challenge or X-prize for data mining? KDD 2006: 954-956 - [c23]Rezarta Islamaj Dogan, Lise Getoor, W. John Wilbur:
A Feature Generation Algorithm for Sequences with Application to Splice-Site Prediction. PKDD 2006: 553-560 - [c22]Indrajit Bhattacharya, Lise Getoor:
A Latent Dirichlet Model for Unsupervised Entity Resolution. SDM 2006: 47-58 - [c21]Christopher P. Diehl, Lise Getoor, Galileo Namata:
Name Reference Resolution in Organizational Email Archives. SDM 2006: 70-81 - [e2]Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Stephen H. Muggleton:
Probabilistic, Logical and Relational Learning - Towards a Synthesis, 30. January - 4. February 2005. Dagstuhl Seminar Proceedings 05051, Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany 2006 [contents] - 2005
- [j8]Lise Getoor, Christopher P. Diehl:
Introduction to the special issue on link mining. SIGKDD Explor. 7(2): 1-2 (2005) - [j7]Lise Getoor, Christopher P. Diehl:
Link mining: a survey. SIGKDD Explor. 7(2): 3-12 (2005) - [c20]Marie desJardins, Priyang Rathod, Lise Getoor:
Bayesian Network Learning with Abstraction Hierarchies and Context-Specific Independence. ECML 2005: 485-496 - [c19]Mustafa Bilgic, Louis Licamele, Lise Getoor, Ben Shneiderman:
D-Dupe: An Interactive Tool for Entity Resolution in Social Networks. GD 2005: 505-507 - [c18]Lisa Singh, Lise Getoor, Louis Licamele:
Pruning Social Networks Using Structural Properties and Descriptive Attributes. ICDM 2005: 773-776 - [c17]Lise Getoor:
Tutorial on Statistical Relational Learning. ILP 2005: 415 - [c16]Louis Licamele, Mustafa Bilgic, Lise Getoor, Nick Roussopoulos:
Capital and benefit in social networks. LinkKDD 2005: 44-51 - [i2]Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Stephen H. Muggleton:
05051 Executive Summary - Probabilistic, Logical and Relational Learning - Towards a Synthesis. Probabilistic, Logical and Relational Learning 2005 - [i1]Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Stephen H. Muggleton:
05051 Abstracts Collection - Probabilistic, Logical and Relational Learning - Towards a Synthesis. Probabilistic, Logical and Relational Learning 2005 - 2004
- [j6]Lise Getoor, Jeanne T. Rhee, Daphne Koller, Peter Small:
Understanding tuberculosis epidemiology using structured statistical models. Artif. Intell. Medicine 30(3): 233-256 (2004) - [j5]Andreas Hotho, York Sure, Lise Getoor:
A workshop report: mining for and from the Semantic Web at KDD 2004. SIGKDD Explor. 6(2): 142-143 (2004) - [c15]Indrajit Bhattacharya, Lise Getoor, Yoshua Bengio:
Unsupervised Sense Disambiguation Using Bilingual Probabilistic Models. ACL 2004: 287-294 - [c14]Indrajit Bhattacharya, Lise Getoor:
Iterative record linkage for cleaning and integration. DMKD 2004: 11-18 - [c13]Kristina Lerman, Lise Getoor, Steven Minton, Craig A. Knoblock:
Using the Structure of Web Sites for Automatic Segmentation of Tables. SIGMOD Conference 2004: 119-130 - 2003
- [j4]Lise Getoor:
Learning Structure From Statistical Models. IEEE Data Eng. Bull. 26(3): 11-18 (2003) - [j3]Lise Getoor:
Link mining: a new data mining challenge. SIGKDD Explor. 5(1): 84-89 (2003) - [c12]Edward Hung, Lise Getoor, V. S. Subrahmanian:
PXML: A Probabilistic Semistructured Data Model and Algebra. ICDE 2003: 467-478 - [c11]Edward Hung, Lise Getoor, V. S. Subrahmanian:
Probabilistic Interval XML. ICDT 2003: 358-374 - [c10]Qing Lu, Lise Getoor:
Link-based Classification. ICML 2003: 496-503 - [e1]Lise Getoor, Ted E. Senator, Pedro M. Domingos, Christos Faloutsos:
Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 24 - 27, 2003. ACM 2003, ISBN 1-58113-737-0 [contents] - 2002
- [j2]Lise Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar:
Learning Probabilistic Models of Link Structure. J. Mach. Learn. Res. 3: 679-707 (2002) - 2001
- [b1]Lise Getoor:
Learning statistical models from relational data. Stanford University, USA, 2001 - [j1]Yves Lespérance, Gerd Wagner, William P. Birmingham, Kurt D. Bollacker, Alexander Nareyek, J. Paul Walser, David W. Aha, Timothy W. Finin, Benjamin N. Grosof, Nathalie Japkowicz, Robert Holte, Lise Getoor, Carla P. Gomes, Holger H. Hoos, Alan C. Schultz, Miroslav Kubat, Tom M. Mitchell, Jörg Denzinger, Yolanda Gil, Karen L. Myers, Claudio Bettini, Angelo Montanari:
AAAI 2000 Workshop Reports. AI Mag. 22(1): 127-136 (2001) - [c9]Lise Getoor, Nir Friedman, Daphne Koller, Benjamin Taskar:
Learning Probabilistic Models of Relational Structure. ICML 2001: 170-177 - [c8]Lise Getoor, Benjamin Taskar, Daphne Koller:
Selectivity Estimation using Probabilistic Models. SIGMOD Conference 2001: 461-472 - 2000
- [c7]Marie desJardins, Lise Getoor, Daphne Koller:
Using Feature Hierarchies in Bayesian Network Learning. SARA 2000: 260-270 - [c6]Lise Getoor:
Learning Probabilistic Relational Models. SARA 2000: 322-323
1990 – 1999
- 1999
- [c5]Nir Friedman, Lise Getoor:
Efficient learning using constrained sufficient statistics. AISTATS 1999 - [c4]Nir Friedman, Lise Getoor, Daphne Koller, Avi Pfeffer:
Learning Probabilistic Relational Models. IJCAI 1999: 1300-1309 - 1998
- [c3]Urszula Chajewska, Lise Getoor, Joseph Norman, Yuval Shahar:
Utility Elicitation as a Classification Problem. UAI 1998: 79-88 - 1997
- [c2]Lise Getoor, Greger Ottosson, Markus P. J. Fromherz, Björn Carlson:
Effective Redundant Constraints for Online Scheduling. AAAI/IAAI 1997: 302-307 - 1995
- [c1]Amy L. Lansky, Lise Getoor:
Scope and Abstraction: Two Criteria for Localized Planning. IJCAI 1995: 1612-1619
Coauthor Index
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