| Are we evaluating rigorously? benchmarking recommendation for reproducible evaluation and fair comparison Z Sun, D Yu, H Fang, J Yang, X Qu, J Zhang, C Geng Fourteenth ACM conference on recommender systems, 23-32, 2020 | 154 | 2020 |
| An improved TLBO based memetic algorithm for aerodynamic shape optimization X Qu, R Zhang, B Liu, H Li Engineering Applications of Artificial Intelligence 57, 1-15, 2017 | 53 | 2017 |
| Minimalistic attacks: How little it takes to fool deep reinforcement learning policies X Qu, Z Sun, YS Ong, A Gupta, P Wei IEEE Transactions on Cognitive and Developmental Systems 13 (4), 806-817, 2020 | 36 | 2020 |
| Large language models as evolutionary optimizers S Liu, C Chen, X Qu, K Tang, YS Ong arXiv preprint arXiv:2310.19046, 2023 | 23 | 2023 |
| Revisiting Bundle Recommendation: Datasets, Tasks, Challenges and Opportunities for Intent-aware Product Bundling Z Sun, J Yang, K Feng, H Fang, X Qu, YS Ong SIGIR Proceedings of the 45th International ACM SIGIR Conference on Research …, 2022 | 23 | 2022 |
| Next Point-of-Interest Recommendation with Inferring Multi-step Future Preferences L Zhang, Z Sun, Z Wu, J Zhang, YS Ong, X Qu IJCAI, 2022 | 22 | 2022 |
| DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation Z Sun, H Fang, J Yang, X Qu, H Liu, D Yu, YS Ong, J Zhang IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022 | 19 | 2022 |
| Language adaptive cross-lingual speech representation learning with sparse sharing sub-networks Y Lu, M Huang, X Qu, P Wei, Z Ma ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 19 | 2022 |
| Subdomain adaptation with manifolds discrepancy alignment P Wei, Y Ke, X Qu, TY Leong IEEE Transactions on Cybernetics 52 (11), 11698-11708, 2021 | 18 | 2021 |
| Iterated local search for distributed multiple assembly no-wait flowshop scheduling P Li, Y Yang, X Du, X Qu, K Wang, B Liu 2017 IEEE Congress on evolutionary computation (CEC), 1565-1571, 2017 | 18 | 2017 |
| Transfer Kernel Learning for Multi-Source Transfer Gaussian Process Regression P Wei, TV Vo, X Qu, YS Ong, Z Ma IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022 | 15* | 2022 |
| Memetic multi-agent optimization in high dimensions using random embeddings Y Hou, N Jiang, H Ge, Q Zhang, X Qu, L Feng, A Gupta 2019 IEEE Congress on Evolutionary Computation (CEC), 135-141, 2019 | 12 | 2019 |
| Frame-correlation transfers trigger economical attacks on deep reinforcement learning policies X Qu, YS Ong, A Gupta IEEE Transactions on Cybernetics 52 (8), 7577-7590, 2021 | 10 | 2021 |
| Generative multiform Bayesian optimization Z Guo, H Liu, YS Ong, X Qu, Y Zhang, J Zheng IEEE Transactions on Cybernetics 53 (7), 4347-4360, 2022 | 9 | 2022 |
| Top-aware recommender distillation with deep reinforcement learning H Liu, Z Sun, X Qu, F Yuan Information Sciences 576, 642-657, 2021 | 9 | 2021 |
| Memetic evolution strategy for reinforcement learning X Qu, YS Ong, Y Hou, X Shen 2019 IEEE congress on evolutionary computation (CEC), 1922-1928, 2019 | 9 | 2019 |
| A novel improved teaching-learning based optimization for functional optimization X Qu, B Liu, Z Li, W Duan, R Zhang, W Zhang, H Li 2016 12th IEEE International Conference on Control and Automation (ICCA …, 2016 | 9 | 2016 |
| Unsupervised video domain adaptation: A disentanglement perspective P Wei, L Kong, X Qu, X Yin, Z Xu, J Jiang, Z Ma arXiv preprint arXiv:2208.07365, 2022 | 8 | 2022 |
| Large Language Models for Intent-Driven Session Recommendations Z Sun, H Liu, X Qu, K Feng, Y Wang, YS Ong arXiv preprint arXiv:2312.07552, 2023 | 7 | 2023 |
| Adversary Agnostic Robust Deep Reinforcement Learning X Qu, A Gupta, YS Ong, Z Sun IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021 | 7 | 2021 |
