| Correlation-aware multi-label active learning for web service tag recommendation W Shi, X Liu, Q Yu 2017 IEEE International conference on web services (ICWS), 229-236, 2017 | 41 | 2017 |
| Multifaceted uncertainty estimation for label-efficient deep learning W Shi, X Zhao, F Chen, Q Yu Advances in neural information processing systems 33, 17247-17257, 2020 | 30 | 2020 |
| Presenting and evaluating the impact of experiential learning in computing accessibility education Y El-Glaly, W Shi, S Malachowsky, Q Yu, DE Krutz Proceedings of the ACM/IEEE 42nd International Conference on Software …, 2020 | 28 | 2020 |
| Integrating bayesian and discriminative sparse kernel machines for multi-class active learning W Shi, Q Yu Advances in neural information processing systems 32, 2019 | 18 | 2019 |
| Experiential learning in computing accessibility education W Shi, S Khan, Y El-Glaly, S Malachowsky, Q Yu, DE Krutz Proceedings of the ACM/IEEE 42nd International Conference on Software …, 2020 | 16 | 2020 |
| Integrating multi-level tag recommendation with external knowledge bases for automatic question answering E Lima, W Shi, X Liu, Q Yu ACM Transactions on Internet Technology (TOIT) 19 (3), 1-22, 2019 | 16 | 2019 |
| A Bayesian learning model for design-phase service mashup popularity prediction M Alshangiti, W Shi, X Liu, Q Yu Expert Systems with Applications 149, 113231, 2020 | 8 | 2020 |
| A Gaussian process-Bayesian Bernoulli mixture model for multi-label active learning W Shi, D Yu, Q Yu Advances in Neural Information Processing Systems 34, 27542-27554, 2021 | 6 | 2021 |
| Presenting and evaluating the impact of experiential learning in computing accessibility education. In 2020 IEEE/ACM 42nd International Conference on Software Engineering … YN Elglaly, W Shi, S Malachowsky, Q Yu, DE Krutz IEEE, 2020 | 6 | 2020 |
| Fast direct search in an optimally compressed continuous target space for efficient multi-label active learning W Shi, Q Yu International Conference on Machine Learning, 5769-5778, 2019 | 6 | 2019 |
| From novice to expert narratives of dermatological disease N Obot, L O’Malley, I Nwogu, Q Yu, WS Shi, X Guo 2018 IEEE International Conference on Pervasive Computing and Communications …, 2018 | 4 | 2018 |
| Statistical learning of domain-specific quality-of-service features from user reviews X Liu, W Shi, A Kale, C Ding, Q Yu ACM Transactions on Internet Technology (TOIT) 17 (2), 1-24, 2017 | 4 | 2017 |
| Hierarchical bayesian multi-kernel learning for integrated classification and summarization of app reviews M Alshangiti, W Shi, E Lima, X Liu, Q Yu Proceedings of the 30th ACM Joint European Software Engineering Conference …, 2022 | 3 | 2022 |
| Uncertainty-Aware Multiple Instance Learning from Large-Scale Long Time Series Data Y Zhu, W Shi, DS Pandey, Y Liu, X Que, DE Krutz, Q Yu 2021 IEEE International Conference on Big Data (Big Data), 1772-1778, 2021 | 3 | 2021 |
| An efficient many-class active learning framework for knowledge-rich domains W Shi, Q Yu 2018 IEEE International Conference on Data Mining (ICDM), 1230-1235, 2018 | 2 | 2018 |
| ALL: Supporting Experiential Accessibility Education and Inclusive Software Development W Shi, H Moses, Q Yu, S Malachowsky, DE Krutz ACM Transactions on Software Engineering and Methodology 33 (2), 1-30, 2023 | 1 | 2023 |
| Active learning with maximum margin sparse gaussian processes W Shi, Q Yu International Conference on Artificial Intelligence and Statistics, 406-414, 2021 | 1 | 2021 |
| Actively Testing Your Model While It Learns: Realizing Label-Efficient Learning in Practice D Yu, W Shi, Q Yu Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| STARS: spatial-temporal active re-sampling for label-efficient learning from noisy annotations D Yu, W Shi, Q Yu Proceedings of the AAAI Conference on Artificial Intelligence 37 (9), 10980 …, 2023 | | 2023 |
| Discover-then-rank unlabeled support vectors in the dual space for multi-class active learning D Yu, W Shi Fortieth International Conference on Machine Learning, 2023 | | 2023 |
