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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
- 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 R. 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@SenSys 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]