| Underspecification presents challenges for credibility in modern machine learning A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ... arXiv preprint arXiv:2011.03395, 2020 | 297 | 2020 |
| ESCAPES: evacuation simulation with children, authorities, parents, emotions, and social comparison. J Tsai, N Fridman, E Bowring, M Brown, S Epstein, GA Kaminka, ... AAMAS 11, 457-464, 2011 | 232 | 2011 |
| ToTTo: A Controlled Table-To-Text Generation Dataset AP Parikh, X Wang, S Gehrmann, M Faruqui, B Dhingra, D Yang, D Das EMNLP 2020, 2020 | 123 | 2020 |
| Fairness without demographics through adversarially reweighted learning P Lahoti, A Beutel, J Chen, K Lee, F Prost, N Thain, X Wang, EH Chi 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 2020 | 119 | 2020 |
| Adana: Active name disambiguation X Wang, J Tang, H Cheng, SY Philip 2011 IEEE 11th international conference on data mining, 794-803, 2011 | 102 | 2011 |
| Palm: Scaling language modeling with pathways A Chowdhery, S Narang, J Devlin, M Bosma, G Mishra, A Roberts, ... arXiv preprint arXiv:2204.02311, 2022 | 100 | 2022 |
| Flexible transfer learning under support and model shift X Wang, J Schneider Advances in Neural Information Processing Systems 27, 2014 | 92 | 2014 |
| Biperpedia: An ontology for search applications R Gupta, A Halevy, X Wang, S Whang, F Wu | 89 | 2014 |
| Active transfer learning under model shift X Wang, TK Huang, J Schneider International Conference on Machine Learning, 1305-1313, 2014 | 88 | 2014 |
| Relevant document discovery for fact-checking articles X Wang, C Yu, S Baumgartner, F Korn Companion Proceedings of the The Web Conference 2018, 525-533, 2018 | 64 | 2018 |
| Active search on graphs X Wang, R Garnett, J Schneider Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013 | 44 | 2013 |
| Chain of thought prompting elicits reasoning in large language models J Wei, X Wang, D Schuurmans, M Bosma, E Chi, Q Le, D Zhou arXiv preprint arXiv:2201.11903, 2022 | 36* | 2022 |
| CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation T Wang, X Wang, Y Qin, B Packer, K Li, J Chen, A Beutel, E Chi EMNLP 2020, 2020 | 35 | 2020 |
| Measuring and reducing gendered correlations in pre-trained models K Webster, X Wang, I Tenney, A Beutel, E Pitler, E Pavlick, J Chen, E Chi, ... arXiv preprint arXiv:2010.06032, 2020 | 32 | 2020 |
| Transfer of machine learning fairness across domains C Schumann, X Wang, A Beutel, J Chen, H Qian, EH Chi arXiv preprint arXiv:1906.09688, 2019 | 28 | 2019 |
| Evaluating fairness of machine learning models under uncertain and incomplete information P Awasthi, A Beutel, M Kleindessner, J Morgenstern, X Wang Proceedings of the 2021 ACM Conference on Fairness, Accountability, and …, 2021 | 26 | 2021 |
| Measuring recommender system effects with simulated users S Yao, Y Halpern, N Thain, X Wang, K Lee, F Prost, EH Chi, J Chen, ... arXiv preprint arXiv:2101.04526, 2021 | 20 | 2021 |
| Verifying text summaries of relational data sets S Jo, I Trummer, W Yu, X Wang, C Yu, D Liu, N Mehta Proceedings of the 2019 International Conference on Management of Data, 299-316, 2019 | 17 | 2019 |
| Practical compositional fairness: Understanding fairness in multi-component recommender systems X Wang, N Thain, A Sinha, F Prost, EH Chi, J Chen, A Beutel Proceedings of the 14th ACM International Conference on Web Search and Data …, 2021 | 15* | 2021 |
| Mining an" anti-knowledge base" from Wikipedia updates with applications to fact checking and beyond G Karagiannis, I Trummer, S Jo, S Khandelwal, X Wang, C Yu Proceedings of the VLDB Endowment 13 (4), 561-573, 2019 | 13 | 2019 |
Ed H. ChiGoogle Research / Google BrainVerified email at acm.org
