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Xinyang Yi
Xinyang Yi
Google DeepMind
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Title
Cited by
Cited by
Year
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
8012018
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
3272019
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
2892016
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
1842019
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
182*2021
Alternating minimization for mixed linear regression
X Yi, C Caramanis, S Sanghavi
International Conference on Machine Learning, 613-621, 2014
1532014
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
1222020
Regularized em algorithms: A unified framework and statistical guarantees
X Yi, C Caramanis
Advances in Neural Information Processing Systems 28, 2015
992015
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
842020
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
792014
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
722021
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
602016
Learning to embed categorical features without embedding tables for recommendation
WC Kang, DZ Cheng, T Yao, X Yi, T Chen, L Hong, EH Chi
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021
482021
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
472018
Binary embedding: Fundamental limits and fast algorithm
X Yi, C Caramanis, E Price
International Conference on Machine Learning, 2162-2170, 2015
472015
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
452020
Optimal linear estimation under unknown nonlinear transform
X Yi, Z Wang, C Caramanis, H Liu
Advances in neural information processing systems 28, 2015
362015
Distributionally-robust Recommendations for Improving Worst-case User Experience
H Wen, X Yi, T Yao, J Tang, L Hong, EH Chi
Proceedings of the ACM Web Conference 2022, 3606-3610, 2022
252022
Minimax gaussian classification & clustering
T Li, X Yi, C Carmanis, P Ravikumar
Artificial Intelligence and Statistics, 1-9, 2017
212017
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
162017
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