| Deep canonical correlation analysis G Andrew, R Arora, J Bilmes, K Livescu International conference on machine learning, 1247-1255, 2013 | 2324 | 2013 |
| Scalable training of L1-regularized log-linear models G Andrew, J Gao Proceedings of the 24th international conference on Machine learning, 33-40, 2007 | 856 | 2007 |
| Applied federated learning: Improving google keyboard query suggestions T Yang, G Andrew, H Eichner, H Sun, W Li, N Kong, D Ramage, ... arXiv preprint arXiv:1812.02903, 2018 | 711 | 2018 |
| A conditional random field word segmenter for sighan bakeoff 2005 H Tseng, PC Chang, G Andrew, D Jurafsky, CD Manning Proceedings of the fourth SIGHAN workshop on Chinese language Processing, 2005 | 600 | 2005 |
| Tregex and Tsurgeon: Tools for querying and manipulating tree data structures. R Levy, G Andrew LREC, 2231-2234, 2006 | 505 | 2006 |
| A field guide to federated optimization J Wang, Z Charles, Z Xu, G Joshi, HB McMahan, M Al-Shedivat, G Andrew, ... arXiv preprint arXiv:2107.06917, 2021 | 365 | 2021 |
| Differentially private learning with adaptive clipping G Andrew, O Thakkar, B McMahan, S Ramaswamy Advances in Neural Information Processing Systems 34, 17455-17466, 2021 | 254 | 2021 |
| A portfolio approach to algorithm selection K Leyton-Brown, E Nudelman, G Andrew, J McFadden, Y Shoham IJCAI 3, 1542-1543, 2003 | 242 | 2003 |
| A general approach to adding differential privacy to iterative training procedures HB McMahan, G Andrew, U Erlingsson, S Chien, I Mironov, N Papernot, ... arXiv preprint arXiv:1812.06210, 2018 | 220 | 2018 |
| Interactive control of diverse complex characters with neural networks I Mordatch, K Lowrey, G Andrew, Z Popovic, EV Todorov Advances in neural information processing systems 28, 2015 | 138 | 2015 |
| Query suggestion generation G Andrew, S Park, RL Rounthwaite, SP Cucerzan, JP Buckley, J Chan US Patent 7,984,004, 2011 | 123 | 2011 |
| Differentially private learning with adaptive clipping O Thakkar, G Andrew, HB McMahan arXiv e-prints, arXiv: 1905.03871, 2019 | 112 | 2019 |
| A hybrid markov/semi-markov conditional random field for sequence segmentation G Andrew Proceedings of the 2006 Conference on Empirical Methods in Natural Language …, 2006 | 95 | 2006 |
| Boosting as a metaphor for algorithm design K Leyton-Brown, E Nudelman, G Andrew, J McFadden, Y Shoham International Conference on Principles and Practice of Constraint …, 2003 | 92 | 2003 |
| Training production language models without memorizing user data S Ramaswamy, O Thakkar, R Mathews, G Andrew, HB McMahan, ... arXiv preprint arXiv:2009.10031, 2020 | 78 | 2020 |
| A comparative study of parameter estimation methods for statistical natural language processing J Gao, G Andrew, M Johnson, K Toutanova Annual Meeting of the Association for Computational Linguistics (ACL), 2007 | 72 | 2007 |
| Federated learning of gboard language models with differential privacy Z Xu, Y Zhang, G Andrew, CA Choquette-Choo, P Kairouz, HB McMahan, ... arXiv preprint arXiv:2305.18465, 2023 | 66 | 2023 |
| Weighted linear model R Moore, W Yih, G Andrew, K Toutanova US Patent App. 11/485,015, 2007 | 60 | 2007 |
| Determining a similarity measure between queries RL Rounthwaite, G Andrew, EM Kiciman, X Yin US Patent 8,606,786, 2013 | 49 | 2013 |
| TensorFlow privacy G Andrew, S Chien, N Papernot Accessed 6, 22, 2019 | 29 | 2019 |
