| Towards Robust and Privacy-preserving Text Representations Y Li, T Baldwin, T Cohn ACL 2018, 2018 | 48 | 2018 |
| Robust Training under Linguistic Adversity Y Li, T Cohn, T Baldwin Proceedings of the 15th Conference of the European Chapter of the …, 2017 | 26 | 2017 |
| 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 | 20 | 2020 |
| What's in a Domain? Learning Domain-Robust Text Representations using Adversarial Training Y Li, T Baldwin, T Cohn NAACL 2018, 2018 | 19 | 2018 |
| 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 | 14 | 2018 |
| 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 | 11 | 2016 |
| Learning Robust Representations of Text Y Li, T Cohn, T Baldwin EMNLP 2016, 2016 | 10 | 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 | 9 | 2015 |
| 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, 2020 | 6 | 2020 |
| 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 | 5 | 2019 |
| Towards fair and decentralized privacy-preserving deep learning with blockchain L Lyu, J Yu, K Nandakumar, Y Li, X Ma, J Jin arXiv preprint arXiv:1906.01167, 1-13, 2019 | 5 | 2019 |
| 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 | 3 | 2020 |
| Towards Fair and Decentralized Privacy-Preserving Deep Learning L Lyu, J Yu, K Nandakumar, Y Li, X Ma, J Jin arXiv: 1906.01167 v2 [cs. CR], 2019 | 2 | 2019 |
| Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness L Lyu, X He, Y Li arXiv preprint arXiv:2010.01285, 2020 | 1 | 2020 |
| Bibi system description: Building with cnns and breaking with deep reinforcement learning Y Li, T Cohn, T Baldwin Proceedings of the First Workshop on Building Linguistically Generalizable …, 2017 | 1 | 2017 |
| Filter and Match Approach to Pair-wise Web URI Linking S Subramanian, Y Li, A Rahimi Proceedings of the Australasian Language Technology Association Workshop …, 2016 | 1 | 2016 |
| Towards Robust Representation of Natural Language Processing Y Li | | 2019 |
| Multi-task Adversarial Representation Learning for Text Classification Y Li, T Cohn, T Baldwin School of Computing and Information Systems 5th Annual Doctoral Colloquium, 3, 2017 | | 2017 |
Timothy BaldwinSchool of Computing and Information Systems, The University of MelbourneVerified email at unimelb.edu.au
