| Towards fair and privacy-preserving federated deep models L Lyu, J Yu, K Nandakumar, Y Li, X Ma, J Jin, H Yu, KS Ng IEEE Transactions on Parallel and Distributed Systems 31 (11), 2524-2541, 2020 | 177* | 2020 |
| Towards Robust and Privacy-preserving Text Representations Y Li, T Baldwin, T Cohn ACL 2018, 2018 | 156 | 2018 |
| Robust Training under Linguistic Adversity Y Li, T Cohn, T Baldwin Proceedings of the 15th Conference of the European Chapter of the …, 2017 | 50 | 2017 |
| What's in a Domain? Learning Domain-Robust Text Representations using Adversarial Training Y Li, T Baldwin, T Cohn NAACL 2018, 2018 | 42 | 2018 |
| Differentially private representation for nlp: Formal guarantee and an empirical study on privacy and fairness L Lyu, X He, Y Li EMNLP 2020 findings, 2020 | 36 | 2020 |
| Detecting misflagged duplicate questions in community question-answering archives D Hoogeveen, A Bennett, Y Li, K Verspoor, T Baldwin Proceedings of the International AAAI Conference on Web and Social Media 12 (1), 2018 | 32 | 2018 |
| How to democratise and protect AI: Fair and differentially private decentralised deep learning L Lyu, Y Li, K Nandakumar, J Yu, X Ma IEEE Transactions on Dependable and Secure Computing 19 (2), 1003-1017, 2020 | 31 | 2020 |
| Towards differentially private text representations L Lyu, Y Li, X He, T Xiao Proceedings of the 43rd International ACM SIGIR Conference on Research and …, 2020 | 30 | 2020 |
| CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems F Mi, Y Li, Y Wang, X Jiang, Q Liu AAAI 2022, 2021 | 21 | 2021 |
| Learning Robust Representations of Text Y Li, T Cohn, T Baldwin EMNLP 2016, 2016 | 16 | 2016 |
| A machine learning method to distinguish machine translation from human translation Y Li, R Wang, H Zhao Proceedings of the 29th Pacific Asia conference on language, information and …, 2015 | 16 | 2015 |
| Semi-supervised Stochastic Multi-Domain Learning using Variational Inference Y Li, T Baldwin, T Cohn Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019 | 14* | 2019 |
| UniMelb at SemEval-2016 Task 3: Identifying similar questions by combining a CNN with string similarity measures T Baldwin, H Liang, B Salehi, D Hoogeveen, Y Li, L Duong Proceedings of the 10th International Workshop on Semantic Evaluation …, 2016 | 14 | 2016 |
| Compilable Neural Code Generation with Compiler Feedback X Wang, Y Wang, Y Wan, F Mi, Y Li, P Zhou, J Liu, H Wu, X Jiang, Q Liu acl 2022 findings, 2022 | 13 | 2022 |
| PanGu-Bot: Efficient Generative Dialogue Pre-training from Pre-trained Language Model F Mi, Y Li, Y Zeng, J Zhou, Y Wang, C Xu, L Shang, X Jiang, S Zhao, Q Liu arXiv preprint arXiv:2203.17090, 2022 | 12 | 2022 |
| RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion K Chen, Y Wang, Y Li, A Li ACL 2022 main, 2022 | 12 | 2022 |
| FairLib: A unified framework for assessing and improving fairness X Han, A Shen, Y Li, L Frermann, T Baldwin, T Cohn Proceedings of the The 2022 Conference on Empirical Methods in Natural …, 2022 | 9* | 2022 |
| Towards identifying social bias in dialog systems: Frame, datasets, and benchmarks J Zhou, J Deng, F Mi, Y Li, Y Wang, M Huang, X Jiang, Q Liu, H Meng EMNLP 2022 findings, 2022 | 8 | 2022 |
| UniDS: A unified dialogue system for chit-chat and task-oriented dialogues X Zhao, B He, Y Wang, Y Li, F Mi, Y Liu, X Jiang, Q Liu, H Chen arXiv preprint arXiv:2110.08032, 2021 | 8 | 2021 |
| Uncertainty-Aware Balancing for Multilingual and Multi-Domain Neural Machine Translation Training M Wu, Y Li, M Zhang, L Li, G Haffari, Q Liu EMNLP 2021, 2021 | 6 | 2021 |
Timothy BaldwinMBZUAI and The University of MelbourneGeverifieerd e-mailadres voor unimelb.edu.au
