| Autogen: Enabling next-gen llm applications via multi-agent conversation framework Q Wu, G Bansal, J Zhang, Y Wu, S Zhang, E Zhu, B Li, L Jiang, X Zhang, ... arXiv preprint arXiv:2308.08155, 2023 | 432 | 2023 |
| Estimation-action-reflection: Towards deep interaction between conversational and recommender systems W Lei, X He, Y Miao, Q Wu, R Hong, MY Kan, TS Chua Proceedings of the 13th International Conference on Web Search and Data …, 2020 | 287 | 2020 |
| Flaml: A fast and lightweight automl library C Wang, Q Wu, M Weimer, E Zhu Proceedings of Machine Learning and Systems 3, 434-447, 2021 | 185 | 2021 |
| Factorization bandits for interactive recommendation H Wang, Q Wu, H Wang Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 127 | 2017 |
| Contextual Bandits in a Collaborative Environment Q Wu, H Wang, Q Gu, H Wang The 39th International ACM SIGIR conference on Research and Development in …, 2016 | 124 | 2016 |
| Seamlessly unifying attributes and items: Conversational recommendation for cold-start users S Li, W Lei, Q Wu, X He, P Jiang, TS Chua ACM Transactions on Information Systems (TOIS) 39 (4), 1-29, 2021 | 122 | 2021 |
| Learning contextual bandits in a non-stationary environment Q Wu, N Iyer, H Wang The 41st International ACM SIGIR Conference on Research & Development in …, 2018 | 100 | 2018 |
| Learning Hidden Features for Contextual Bandits H Wang, Q Wu, H Wang CIKM '16, 1633-1642, 2016 | 91 | 2016 |
| Returning is believing: Optimizing long-term user engagement in recommender systems Q Wu, H Wang, L Hong, Y Shi Proceedings of the 2017 ACM on Conference on Information and Knowledge …, 2017 | 90 | 2017 |
| Fast distributed bandits for online recommendation systems K Mahadik, Q Wu, S Li, A Sabne Proceedings of the 34th ACM international conference on supercomputing, 1-13, 2020 | 67 | 2020 |
| Factorization bandits for online influence maximization Q Wu, Z Li, H Wang, W Chen, H Wang Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 45 | 2019 |
| Variance reduction in gradient exploration for online learning to rank H Wang, S Kim, E McCord-Snook, Q Wu, H Wang Proceedings of the 42nd International ACM SIGIR Conference on Research and …, 2019 | 40 | 2019 |
| Frugal optimization for cost-related hyperparameters Q Wu, C Wang, S Huang Proceedings of the AAAI Conference on Artificial Intelligence 35 (12), 10347 …, 2021 | 39 | 2021 |
| An empirical study on challenging math problem solving with gpt-4 Y Wu, F Jia, S Zhang, H Li, E Zhu, Y Wang, YT Lee, R Peng, Q Wu, ... arXiv preprint arXiv:2306.01337, 2023 | 35 | 2023 |
| Global and local differential privacy for collaborative bandits H Wang, Q Zhao, Q Wu, S Chopra, A Khaitan, H Wang Proceedings of the 14th ACM Conference on Recommender Systems, 150-159, 2020 | 34 | 2020 |
| Dynamic ensemble of contextual bandits to satisfy users' changing interests Q Wu, H Wang, Y Li, H Wang The World Wide Web Conference, 2080-2090, 2019 | 32 | 2019 |
| Economic hyperparameter optimization with blended search strategy C Wang, Q Wu, S Huang, A Saied International Conference on Learning Representations, 2021 | 28 | 2021 |
| Bandit learning with implicit feedback Y Qi, Q Wu, H Wang, J Tang, M Sun Advances in Neural Information Processing Systems 31, 2018 | 27 | 2018 |
| Targeted hyperparameter optimization with lexicographic preferences over multiple objectives S Zhang, F Jia, C Wang, Q Wu The Eleventh international conference on learning representations, 2023 | 22 | 2023 |
| Ideal: Influence-driven selective annotations empower in-context learners in large language models S Zhang, X Xia, Z Wang, LH Chen, J Liu, Q Wu, T Liu arXiv preprint arXiv:2310.10873, 2023 | 19 | 2023 |
Chi WangMicrosoft ResearchAdresă de e-mail confirmată pe microsoft.com
