| Modeling task relationships in multi-task learning with multi-gate mixture-of-experts J Ma, Z Zhao, X Yi, J Chen, L Hong, EH Chi Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018 | 453 | 2018 |
| Fast algorithms for robust PCA via gradient descent X Yi, D Park, Y Chen, C Caramanis Advances in Neural Information Processing Systems, 361-369, 2016 | 262 | 2016 |
| Recommending what video to watch next: a multitask ranking system Z Zhao, L Hong, L Wei, J Chen, A Nath, S Andrews, A Kumthekar, ... Proceedings of the 13th ACM Conference on Recommender Systems, 43-51, 2019 | 227 | 2019 |
| Alternating minimization for mixed linear regression X Yi, C Caramanis, S Sanghavi International Conference on Machine Learning, 613-621, 2014 | 129 | 2014 |
| Sampling-bias-corrected neural modeling for large corpus item recommendations X Yi, J Yang, L Hong, DZ Cheng, L Heldt, A Kumthekar, Z Zhao, L Wei, ... Proceedings of the 13th ACM Conference on Recommender Systems, 269-277, 2019 | 108 | 2019 |
| Regularized em algorithms: A unified framework and statistical guarantees X Yi, C Caramanis Advances in Neural Information Processing Systems 28, 2015 | 90 | 2015 |
| Self-supervised learning for large-scale item recommendations T Yao, X Yi, DZ Cheng, F Yu, T Chen, A Menon, L Hong, EH Chi, S Tjoa, ... Proceedings of the 30th ACM International Conference on Information …, 2021 | 73 | 2021 |
| A convex formulation for mixed regression with two components: Minimax optimal rates Y Chen, X Yi, C Caramanis Conference on Learning Theory, 560-604, 2014 | 72 | 2014 |
| Mixed negative sampling for learning two-tower neural networks in recommendations J Yang, X Yi, D Zhiyuan Cheng, L Hong, Y Li, S Xiaoming Wang, T Xu, ... Companion Proceedings of the Web Conference 2020, 441-447, 2020 | 56 | 2020 |
| Off-policy learning in two-stage recommender systems J Ma, Z Zhao, X Yi, J Yang, M Chen, J Tang, L Hong, EH Chi Proceedings of The Web Conference 2020, 463-473, 2020 | 56 | 2020 |
| Solving a mixture of many random linear equations by tensor decomposition and alternating minimization X Yi, C Caramanis, S Sanghavi arXiv preprint arXiv:1608.05749, 2016 | 52 | 2016 |
| Binary embedding: Fundamental limits and fast algorithm X Yi, C Caramanis, E Price International Conference on Machine Learning, 2162-2170, 2015 | 46 | 2015 |
| Optimal linear estimation under unknown nonlinear transform X Yi, Z Wang, C Caramanis, H Liu Advances in neural information processing systems 28, 2015 | 36 | 2015 |
| A model of two tales: Dual transfer learning framework for improved long-tail item recommendation Y Zhang, DZ Cheng, T Yao, X Yi, L Hong, EH Chi Proceedings of the web conference 2021, 2220-2231, 2021 | 33 | 2021 |
| Efficient training on very large corpora via gramian estimation W Krichene, N Mayoraz, S Rendle, L Zhang, X Yi, L Hong, E Chi, ... arXiv preprint arXiv:1807.07187, 2018 | 30 | 2018 |
| Learning multi-granular quantized embeddings for large-vocab categorical features in recommender systems WC Kang, DZ Cheng, T Chen, X Yi, D Lin, L Hong, EH Chi Companion Proceedings of the Web Conference 2020, 562-566, 2020 | 27 | 2020 |
| Learning to embed categorical features without embedding tables for recommendation WC Kang, DZ Cheng, T Yao, X Yi, T Chen, L Hong, EH Chi arXiv preprint arXiv:2010.10784, 2020 | 25 | 2020 |
| Self-supervised learning for deep models in recommendations T Yao, X Yi, DZ Cheng, FX Yu, AK Menon, L Hong, EH Chi, S Tjoa, J Kang, ... CoRR, 2020 | 18 | 2020 |
| Minimax gaussian classification & clustering T Li, X Yi, C Carmanis, P Ravikumar Artificial Intelligence and Statistics, 1-9, 2017 | 17 | 2017 |
| Convex and nonconvex formulations for mixed regression with two components: Minimax optimal rates Y Chen, X Yi, C Caramanis IEEE Transactions on Information Theory 64 (3), 1738-1766, 2017 | 14 | 2017 |
Lichan HongGoogle BrainVerified email at google.com
