| Petuum: A new platform for distributed machine learning on big data EP Xing, Q Ho, W Dai, JK Kim, J Wei, S Lee, X Zheng, P Xie, A Kumar, ... Proceedings of the 21th ACM SIGKDD International Conference on Knowledge …, 2015 | 566 | 2015 |
| Semantic pooling for complex event analysis in untrimmed videos X Chang, YL Yu, Y Yang, EP Xing IEEE transactions on pattern analysis and machine intelligence 39 (8), 1617-1632, 2016 | 333 | 2016 |
| DeeBERT: Dynamic early exiting for accelerating BERT inference J Xin, R Tang, J Lee, Y Yu, J Lin arXiv preprint arXiv:2004.12993, 2020 | 184 | 2020 |
| Convex multi-view subspace learning M White, Y Yu, X Zhang, D Schuurmans Advances in Neural Information Processing Systems, 1682-1690, 2012 | 182 | 2012 |
| Semantic concept discovery for large-scale zero-shot event detection X Chang, Y Yang, A Hauptmann, EP Xing, YL Yu Twenty-fourth international joint conference on artificial intelligence, 2015 | 122 | 2015 |
| Accelerated training for matrix-norm regularization: A boosting approach X Zhang, Y Yu, D Schuurmans Advances in Neural Information Processing Systems, 2906-2914, 2012 | 107 | 2012 |
| Sum-of-squares polynomial flow P Jaini, KA Selby, Y Yu International Conference on Machine Learning, 3009-3018, 2019 | 105 | 2019 |
| Analysis of kernel mean matching under covariate shift Y Yu, C Szepesvári ICML, 2012 | 99 | 2012 |
| On decomposing the proximal map YL Yu Advances in neural information processing systems 26, 2013 | 91 | 2013 |
| Complex event detection using semantic saliency and nearly-isotonic SVM X Chang, Y Yang, EP Xing, YL Yu ICML, 2015 | 84 | 2015 |
| Problems and opportunities in training deep learning software systems: An analysis of variance HV Pham, S Qian, J Wang, T Lutellier, J Rosenthal, L Tan, Y Yu, ... Proceedings of the 35th IEEE/ACM international conference on automated …, 2020 | 81 | 2020 |
| Better approximation and faster algorithm using the proximal average YL Yu Advances in neural information processing systems 26, 2013 | 77 | 2013 |
| Distributional reinforcement learning for efficient exploration B Mavrin, H Yao, L Kong, K Wu, Y Yu International conference on machine learning, 4424-4434, 2019 | 68 | 2019 |
| Searching persuasively: Joint event detection and evidence recounting with limited supervision X Chang, YL Yu, Y Yang, AG Hauptmann Proceedings of the 23rd ACM international conference on Multimedia, 581-590, 2015 | 55 | 2015 |
| Relaxed clipping: A global training method for robust regression and classification Y Yu, M Yang, L Xu, M White, D Schuurmans Advances in neural information processing systems, 2532-2540, 2010 | 51 | 2010 |
| Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting. P Xie, JK Kim, Y Zhou, Q Ho, A Kumar, Y Yu, EP Xing UAI, 2016 | 49* | 2016 |
| Generalized conditional gradient for sparse estimation Y Yu, X Zhang, D Schuurmans The Journal of Machine Learning Research 18 (1), 5279-5324, 2017 | 46 | 2017 |
| BERxiT: Early exiting for BERT with better fine-tuning and extension to regression J Xin, R Tang, Y Yu, J Lin Proceedings of the 16th conference of the European chapter of the …, 2021 | 42 | 2021 |
| Additive approximations in high dimensional nonparametric regression via the SALSA K Kandasamy, Y Yu International conference on machine learning, 69-78, 2016 | 41 | 2016 |
| Dropout with expectation-linear regularization X Ma, Y Gao, Z Hu, Y Yu, Y Deng, E Hovy arXiv preprint arXiv:1609.08017, 2016 | 40 | 2016 |
Dale SchuurmansResearch Scientist, Google Brain / Professor, University of AlbertaBestätigte E-Mail-Adresse bei cs.ualberta.ca
