| Hinge-loss Markov random fields: Convex inference for structured prediction S Bach, B Huang, B London, L Getoor arXiv preprint arXiv:1309.6813, 2013 | 127 | 2013 |
| Query-driven active surveying for collective classification G Namata, B London, L Getoor, B Huang, UMD EDU 10th International Workshop on Mining and Learning with Graphs 8, 2012 | 111 | 2012 |
| Collective stability in structured prediction: Generalization from one example B London, B Huang, B Taskar, L Getoor International Conference on Machine Learning, 828-836, 2013 | 48 | 2013 |
| A PAC-Bayesian analysis of randomized learning with application to stochastic gradient descent B London arXiv preprint arXiv:1709.06617, 2017 | 41 | 2017 |
| Collective Classification of Network Data. B London, L Getoor Data Classification: Algorithms and Applications 399, 2014 | 34 | 2014 |
| Stability and generalization in structured prediction B London, B Huang, L Getoor The Journal of Machine Learning Research 17 (1), 7808-7859, 2016 | 31 | 2016 |
| Collective activity detection using hinge-loss Markov random fields B London, S Khamis, S Bach, B Huang, L Getoor, L Davis Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2013 | 26 | 2013 |
| PAC-Bayesian collective stability B London, B Huang, B Taskar, L Getoor Artificial Intelligence and Statistics, 585-594, 2014 | 24 | 2014 |
| Multi-relational learning using weighted tensor decomposition with modular loss B London, T Rekatsinas, B Huang, L Getoor arXiv preprint arXiv:1303.1733, 2013 | 24 | 2013 |
| Collective graph identification GM Namata, B London, L Getoor ACM Transactions on Knowledge Discovery from Data (TKDD) 10 (3), 1-36, 2016 | 19 | 2016 |
| Reducing label cost by combining feature labels and crowdsourcing J Pujara, B London, L Getoor ICML workshop on Combining learning strategies to reduce label cost, 2011 | 15 | 2011 |
| Sustainability at scale: towards bridging the intention-behavior gap with sustainable recommendations S Tomkins, S Isley, B London, L Getoor Proceedings of the 12th ACM conference on recommender systems, 214-218, 2018 | 13 | 2018 |
| The benefits of learning with strongly convex approximate inference B London, B Huang, L Getoor International Conference on Machine Learning, 410-418, 2015 | 11 | 2015 |
| Improved generalization bounds for large-scale structured prediction B London, B Huang, L Getoor NIPS workshop on algorithmic and statistical approaches for large social …, 2012 | 11 | 2012 |
| Budgeted online collective inference J Pujara, B London, L Getoor UAI, 2015 | 10 | 2015 |
| Online inference for knowledge graph construction J Pujara, B London, L Getoor, WW Cohen Fifth International Workshop on Statistical Relational AI, www …, 2015 | 9 | 2015 |
| Generalization bounds for randomized learning with application to stochastic gradient descent B London NIPS Workshop on Optimizing the Optimizers, 2016 | 8 | 2016 |
| Pac-bayes generalization bounds for randomized structured prediction B London, B Huang, B Taskar, L Getoor NIPS Workshop on Perturbation, Optimization and Statistics, 2007 | 8 | 2007 |
| Bayesian counterfactual risk minimization B London, T Sandler International Conference on Machine Learning, 4125-4133, 2019 | 6 | 2019 |
| Generative adversarial structured networks B London, AG Schwing Proc. NIPS Workshop on Adversarial Training, 2016 | 5 | 2016 |
Lise GetoorProfessor of Computer Science, University of California, Santa CruzVerified email at ucsc.edu
