| Posterior regularization for structured latent variable models K Ganchev, J Graça, J Gillenwater, B Taskar The Journal of Machine Learning Research 11, 2001-2049, 2010 | 621 | 2010 |
| Practical diversified recommendations on youtube with determinantal point processes M Wilhelm, A Ramanathan, A Bonomo, S Jain, EH Chi, J Gillenwater Proceedings of the 27th ACM International Conference on Information and …, 2018 | 161 | 2018 |
| Dependency grammar induction via bitext projection constraints K Ganchev, J Gillenwater, B Taskar Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL …, 2009 | 154 | 2009 |
| Near-optimal map inference for determinantal point processes J Gillenwater, A Kulesza, B Taskar Advances in Neural Information Processing Systems 25, 2012 | 149 | 2012 |
| Federated learning via posterior averaging: A new perspective and practical algorithms M Al-Shedivat, J Gillenwater, E Xing, A Rostamizadeh arXiv preprint arXiv:2010.05273, 2020 | 117 | 2020 |
| Discovering diverse and salient threads in document collections J Gillenwater, A Kulesza, B Taskar Proceedings of the 2012 Joint Conference on Empirical Methods in Natural …, 2012 | 113 | 2012 |
| Expectation-maximization for learning determinantal point processes JA Gillenwater, A Kulesza, E Fox, B Taskar Advances in Neural Information Processing Systems 27, 2014 | 112 | 2014 |
| Sparsity in dependency grammar induction J Gillenwater, K Ganchev, J Graça, F Pereira, B Taskar Proceedings of the ACL 2010 Conference Short Papers, 194-199, 2010 | 58 | 2010 |
| Differentially private quantiles J Gillenwater, M Joseph, A Kulesza International Conference on Machine Learning, 3713-3722, 2021 | 45 | 2021 |
| Approximate inference for determinantal point processes J Gillenwater University of Pennsylvania, 2014 | 43 | 2014 |
| Posterior sparsity in unsupervised dependency parsing J Gillenwater, K Ganchev, J Graça, F Pereira, B Taskar The Journal of Machine Learning Research 12, 455-490, 2011 | 39 | 2011 |
| A tree-based method for fast repeated sampling of determinantal point processes J Gillenwater, A Kulesza, Z Mariet, S Vassilvtiskii International Conference on Machine Learning, 2260-2268, 2019 | 29 | 2019 |
| Plume: differential privacy at scale K Amin, J Gillenwater, M Joseph, A Kulesza, S Vassilvitskii arXiv preprint arXiv:2201.11603, 2022 | 20 | 2022 |
| Submodular hamming metrics JA Gillenwater, RK Iyer, B Lusch, R Kidambi, JA Bilmes Advances in Neural Information Processing Systems 28, 2015 | 20 | 2015 |
| Synthesizable high level hardware descriptions: using statically typed two-level languages to guarantee verilog synthesizability J Gillenwater, G Malecha, C Salama, AY Zhu, W Taha, J Grundy, ... Proceedings of the 2008 ACM SIGPLAN symposium on Partial evaluation and …, 2008 | 17 | 2008 |
| Graph-based posterior regularization for semi-supervised structured prediction L He, J Gillenwater, B Taskar Proceedings of the Seventeenth Conference on Computational Natural Language …, 2013 | 16 | 2013 |
| Scalable learning and MAP inference for nonsymmetric determinantal point processes M Gartrell, I Han, E Dohmatob, J Gillenwater, VE Brunel arXiv preprint arXiv:2006.09862, 2020 | 15 | 2020 |
| System and method for audio snippet generation from a subset of music tracks A Subramanya, J Gillenwater, G Griffin, F Pereira, D Eck US Patent 8,666,749, 2014 | 15 | 2014 |
| Posterior regularization for structured latent variable models K Ganchev, J Graca, J Gillenwater, B Taskar Advances in Neural Information Processing Systems 91, 129-136, 2009 | 15 | 2009 |
| Maximizing induced cardinality under a determinantal point process JA Gillenwater, A Kulesza, S Vassilvitskii, ZE Mariet Advances in Neural Information Processing Systems 31, 2018 | 13 | 2018 |
Ben TaskarUniversity of WashingtonVerified email at cs.washington.edu
