| The case for learned index structures T Kraska, A Beutel, EH Chi, J Dean, N Polyzotis Proceedings of the 2018 international conference on management of data, 489-504, 2018 | 1123 | 2018 |
| Recurrent recommender networks CY Wu, A Ahmed, A Beutel, AJ Smola, H Jing Proceedings of the tenth ACM international conference on web search and data …, 2017 | 807 | 2017 |
| Underspecification presents challenges for credibility in modern machine learning A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ... Journal of Machine Learning Research 23 (226), 1-61, 2022 | 728 | 2022 |
| Data decisions and theoretical implications when adversarially learning fair representations A Beutel, J Chen, Z Zhao, EH Chi arXiv preprint arXiv:1707.00075, 2017 | 486 | 2017 |
| Top-k off-policy correction for a REINFORCE recommender system M Chen, A Beutel, P Covington, S Jain, F Belletti, EH Chi Proceedings of the Twelfth ACM International Conference on Web Search and …, 2019 | 478 | 2019 |
| Copycatch: stopping group attacks by spotting lockstep behavior in social networks A Beutel, W Xu, V Guruswami, C Palow, C Faloutsos Proceedings of the 22nd international conference on World Wide Web, 119-130, 2013 | 442 | 2013 |
| Fairness in Recommendation Ranking through Pairwise Comparisons A Beutel, J Chen, T Doshi, H Qian, L Wei, Y Wu, L Heldt, Z Zhao, L Hong, ... Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 411 | 2019 |
| Latent Cross: Making Use of Context in Recurrent Recommender Systems A Beutel, P Covington, S Jain, C Xu, J Li, V Gatto, EH Chi Proceedings of the Eleventh ACM International Conference on Web Search and …, 2018 | 395 | 2018 |
| Fraudar: Bounding graph fraud in the face of camouflage B Hooi, HA Song, A Beutel, N Shah, K Shin, C Faloutsos Proceedings of the 22nd ACM SIGKDD international conference on knowledge …, 2016 | 382 | 2016 |
| Fairness without demographics through adversarially reweighted learning P Lahoti, A Beutel, J Chen, K Lee, F Prost, N Thain, X Wang, E Chi Advances in neural information processing systems 33, 728-740, 2020 | 322 | 2020 |
| Counterfactual fairness in text classification through robustness S Garg, V Perot, N Limtiaco, A Taly, EH Chi, A Beutel Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 219-226, 2019 | 262 | 2019 |
| Sagedb: A learned database system T Kraska, M Alizadeh, A Beutel, EH Chi, J Ding, A Kristo, G Leclerc, ... CIDR, 2019 | 232* | 2019 |
| Catchsync: catching synchronized behavior in large directed graphs M Jiang, P Cui, A Beutel, C Faloutsos, S Yang Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014 | 219 | 2014 |
| Winner takes all: competing viruses or ideas on fair-play networks BA Prakash, A Beutel, R Rosenfeld, C Faloutsos Proceedings of the 21st international conference on World Wide Web, 1037-1046, 2012 | 179 | 2012 |
| Interacting viruses in networks: can both survive? A Beutel, BA Prakash, R Rosenfeld, C Faloutsos Proceedings of the 18th ACM SIGKDD international conference on Knowledge …, 2012 | 158 | 2012 |
| Birdnest: Bayesian inference for ratings-fraud detection B Hooi, N Shah, A Beutel, S Günnemann, L Akoglu, M Kumar, D Makhija, ... Proceedings of the 2016 SIAM International Conference on Data Mining, 495-503, 2016 | 155 | 2016 |
| Putting fairness principles into practice: Challenges, metrics, and improvements A Beutel, J Chen, T Doshi, H Qian, A Woodruff, C Luu, P Kreitmann, ... Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 453-459, 2019 | 149 | 2019 |
| Q&R: A two-stage approach toward interactive recommendation K Christakopoulou, A Beutel, R Li, S Jain, EH Chi Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018 | 139 | 2018 |
| FlexiFaCT: Scalable Flexible Factorization of Coupled Tensors on Hadoop A Beutel, A Kumar, EE Papalexakis, PP Talukdar, C Faloutsos, EP Xing SDM 2, 521-536, 2014 | 139 | 2014 |
| Spotting suspicious link behavior with fbox: An adversarial perspective N Shah, A Beutel, B Gallagher, C Faloutsos 2014 IEEE International conference on data mining, 959-964, 2014 | 132 | 2014 |
Ed H. ChiGoogle DeepMind / Google Brain在 acm.org 的电子邮件经过验证
