Learning Robust Representations of Text. - Yitong Li 002; Trevor Cohn; Timothy Baldwin (EMNLP, 2016)

UniMelb at SemEval-2016 Task 3: Identifying Similar Questions by combining a CNN with String Similarity Measures. - Timothy Baldwin; Huizhi Liang; Bahar Salehi; Doris Hoogeveen; Yitong Li 002; Long Duong (SemEval@NAACL-HLT, 2016)

Robust Training under Linguistic Adversity. - Yitong Li 002; Trevor Cohn; Timothy Baldwin (EACL (2), 2017)

Towards Robust and Privacy-preserving Text Representations. - Yitong Li 002; Timothy Baldwin; Trevor Cohn (ACL (2), 2018)

Detecting Misflagged Duplicate Questions in Community Question-Answering Archives. - Doris Hoogeveen; Andrew Bennett; Yitong Li 002; Karin M. Verspoor; Timothy Baldwin (ICWSM, 2018)

What's in a Domain? Learning Domain-Robust Text Representations using Adversarial Training. - Yitong Li 002; Timothy Baldwin; Trevor Cohn (NAACL-HLT (2), 2018)

Semi-supervised Stochastic Multi-Domain Learning using Variational Inference. - Yitong Li 002; Timothy Baldwin; Trevor Cohn (ACL (1), 2019)

Towards Fair and Decentralized Privacy-Preserving Deep Learning with Blockchain. - Lingjuan Lyu; Jiangshan Yu; Karthik Nandakumar; Yitong Li 002; Xingjun Ma; Jiong Jin (CoRR, 2019)

Towards Robust Representation of Natural Language Processing. - Yitong Li 002 (2019)

Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness. - Lingjuan Lyu; Xuanli He; Yitong Li 002 (EMNLP (Findings), 2020)

Towards Differentially Private Text Representations. - Lingjuan Lyu; Yitong Li 002; Xuanli He; Tong Xiao (SIGIR, 2020)

Towards Fair and Privacy-Preserving Federated Deep Models. - Lingjuan Lyu; Jiangshan Yu; Karthik Nandakumar; Yitong Li 002; Xingjun Ma; Jiong Jin; Han Yu 001; Kee Siong Ng (IEEE Trans. Parallel Distributed Syst., 2020)

FairLib: A Unified Framework for Assessing and Improving Fairness. - Xudong Han; Aili Shen; Yitong Li 002; Lea Frermann; Timothy Baldwin; Trevor Cohn (EMNLP (Demos), 2022)

fairlib: A Unified Framework for Assessing and Improving Classification Fairness. - Xudong Han; Aili Shen; Yitong Li 002; Lea Frermann; Timothy Baldwin; Trevor Cohn (CoRR, 2022)

How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning. - Lingjuan Lyu; Yitong Li 002; Karthik Nandakumar; Jiangshan Yu; Xingjun Ma (IEEE Trans. Dependable Secur. Comput., 2022)