| Datasheets for datasets T Gebru, J Morgenstern, B Vecchione, JW Vaughan, H Wallach, HD Iii, ... Communications of the ACM 64 (12), 86-92, 2021 | 3672 | 2021 |
| Frustratingly easy domain adaptation H Daumé III Proceedings of the 45th annual meeting of the association of computational …, 2007 | 2286 | 2007 |
| Language (technology) is power: A critical survey of" bias" in nlp SL Blodgett, S Barocas, H Daumé Iii, H Wallach arXiv preprint arXiv:2005.14050, 2020 | 1970 | 2020 |
| Co-regularized multi-view spectral clustering A Kumar, P Rai, H Daume Advances in neural information processing systems 24, 2011 | 1583 | 2011 |
| Improving fairness in machine learning systems: What do industry practitioners need? K Holstein, J Wortman Vaughan, H Daumé III, M Dudik, H Wallach Proceedings of the 2019 CHI conference on human factors in computing systems …, 2019 | 1409 | 2019 |
| Deep unordered composition rivals syntactic methods for text classification M Iyyer, V Manjunatha, J Boyd-Graber, H Daumé III Proceedings of the 53rd annual meeting of the association for computational …, 2015 | 1189 | 2015 |
| Domain adaptation for statistical classifiers H Daume III, D Marcu Journal of artificial Intelligence research 26, 101-126, 2006 | 1127 | 2006 |
| A co-training approach for multi-view spectral clustering A Kumar, H Daumé Proceedings of the 28th international conference on machine learning (ICML …, 2011 | 1059 | 2011 |
| Generalized multiview analysis: A discriminative latent space A Sharma, A Kumar, H Daume, DW Jacobs 2012 IEEE conference on computer vision and pattern recognition, 2160-2167, 2012 | 847 | 2012 |
| Search-based structured prediction H Daumé Iii, J Langford, D Marcu Machine learning 75 (3), 297-325, 2009 | 703 | 2009 |
| Learning task grouping and overlap in multi-task learning A Kumar, H Daume III arXiv preprint arXiv:1206.6417, 2012 | 656 | 2012 |
| Midge: Generating image descriptions from computer vision detections M Mitchell, J Dodge, A Goyal, K Yamaguchi, K Stratos, X Han, A Mensch, ... Proceedings of the 13th Conference of the European Chapter of the …, 2012 | 613 | 2012 |
| Corpus-guided sentence generation of natural images Y Yang, C Teo, H Daumé III, Y Aloimonos Proceedings of the 2011 conference on empirical methods in natural language …, 2011 | 554 | 2011 |
| Incorporating lexical priors into topic models J Jagarlamudi, H Daumé III, R Udupa Proceedings of the 13th Conference of the European Chapter of the …, 2012 | 502 | 2012 |
| A neural network for factoid question answering over paragraphs M Iyyer, J Boyd-Graber, L Claudino, R Socher, H Daumé III Proceedings of the 2014 conference on empirical methods in natural language …, 2014 | 488 | 2014 |
| Opponent modeling in deep reinforcement learning H He, J Boyd-Graber, K Kwok, H Daumé III International conference on machine learning, 1804-1813, 2016 | 457 | 2016 |
| When does machine learning {FAIL}? generalized transferability for evasion and poisoning attacks O Suciu, R Marginean, Y Kaya, H Daume III, T Dumitras 27th USENIX Security Symposium (USENIX Security 18), 1299-1316, 2018 | 407 | 2018 |
| Learning to search in branch and bound algorithms H He, H Daumé, J Eisner Advances in neural information processing systems 27, 2014 | 381 | 2014 |
| Bayesian query-focused summarization H Daumé III, D Marcu Proceedings of the 21st International Conference on Computational …, 2006 | 331 | 2006 |
| Learning as search optimization: Approximate large margin methods for structured prediction H Daumé III, D Marcu Proceedings of the 22nd international conference on Machine learning, 169-176, 2005 | 321 | 2005 |
Jordan Boyd-GraberFull Professor, University of MarylandVerified email at umiacs.umd.edu
