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ORCIDYitong Li 0002
Person information
- affiliation: University of Melbourne, Parkville, Victoria, Australia
Other persons with the same name
- Yitong Li — disambiguation page
- Yitong Li 0001 — Apple Inc., USA (and 1 more)
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Books and Theses
- 2019
[b1]Yitong Li:
Towards Robust Representation of Natural Language Processing. University of Melbourne, Parkville, Victoria, Australia, 2019
Journal Articles
- 2022
- [j2]Lingjuan Lyu
, Yitong Li, Karthik Nandakumar, Jiangshan Yu, Xingjun Ma:
How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning. IEEE Trans. Dependable Secur. Comput. 19(2): 1003-1017 (2022) - 2020
- [j1]Lingjuan Lyu, Jiangshan Yu, Karthik Nandakumar, Yitong Li, Xingjun Ma, Jiong Jin, Han Yu, Kee Siong Ng:
Towards Fair and Privacy-Preserving Federated Deep Models. IEEE Trans. Parallel Distributed Syst. 31(11): 2524-2541 (2020)
Conference and Workshop Papers
- 2022
- [c10]Xudong Han, Aili Shen, Yitong Li, Lea Frermann, Timothy Baldwin, Trevor Cohn:
FairLib: A Unified Framework for Assessing and Improving Fairness. EMNLP (Demos) 2022: 60-71 - 2020
- [c9]Lingjuan Lyu, Xuanli He, Yitong Li:
Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness. EMNLP (Findings) 2020: 2355-2365 - [c8]Lingjuan Lyu, Yitong Li, Xuanli He, Tong Xiao:
Towards Differentially Private Text Representations. SIGIR 2020: 1813-1816 - 2019
- [c7]Yitong Li, Timothy Baldwin, Trevor Cohn:
Semi-supervised Stochastic Multi-Domain Learning using Variational Inference. ACL (1) 2019: 1923-1934 - 2018
- [c6]Yitong Li, Timothy Baldwin, Trevor Cohn:
Towards Robust and Privacy-preserving Text Representations. ACL (2) 2018: 25-30 - [c5]Doris Hoogeveen, Andrew Bennett, Yitong Li, Karin M. Verspoor, Timothy Baldwin:
Detecting Misflagged Duplicate Questions in Community Question-Answering Archives. ICWSM 2018: 112-120 - [c4]Yitong Li, Timothy Baldwin, Trevor Cohn:
What's in a Domain? Learning Domain-Robust Text Representations using Adversarial Training. NAACL-HLT (2) 2018: 474-479 - 2017
- [c3]Yitong Li, Trevor Cohn, Timothy Baldwin:
Robust Training under Linguistic Adversity. EACL (2) 2017: 21-27 - 2016
- [c2]Yitong Li, Trevor Cohn, Timothy Baldwin:
Learning Robust Representations of Text. EMNLP 2016: 1979-1985 - [c1]Timothy Baldwin, Huizhi Liang, Bahar Salehi, Doris Hoogeveen, Yitong Li, Long Duong:
UniMelb at SemEval-2016 Task 3: Identifying Similar Questions by combining a CNN with String Similarity Measures. SemEval@NAACL-HLT 2016: 851-856
Informal and Other Publications
- 2022
- [i9]Xudong Han, Aili Shen, Yitong Li, Lea Frermann, Timothy Baldwin, Trevor Cohn:
fairlib: A Unified Framework for Assessing and Improving Classification Fairness. CoRR abs/2205.01876 (2022) - 2020
- [i8]Lingjuan Lyu, Yitong Li, Xuanli He, Tong Xiao:
Towards Differentially Private Text Representations. CoRR abs/2006.14170 (2020) - [i7]Lingjuan Lyu, Yitong Li, Karthik Nandakumar, Jiangshan Yu, Xingjun Ma:
How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning. CoRR abs/2007.09370 (2020) - [i6]Lingjuan Lyu, Xuanli He, Yitong Li:
Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness. CoRR abs/2010.01285 (2020) - 2019
- [i5]Lingjuan Lyu, Jiangshan Yu, Karthik Nandakumar, Yitong Li, Xingjun Ma, Jiong Jin:
Towards Fair and Decentralized Privacy-Preserving Deep Learning with Blockchain. CoRR abs/1906.01167 (2019) - [i4]Yitong Li, Timothy Baldwin, Trevor Cohn:
Semi-supervised Stochastic Multi-Domain Learning using Variational Inference. CoRR abs/1906.02897 (2019) - 2018
- [i3]Yitong Li, Timothy Baldwin, Trevor Cohn:
What's in a Domain? Learning Domain-Robust Text Representations using Adversarial Training. CoRR abs/1805.06088 (2018) - [i2]Yitong Li, Timothy Baldwin, Trevor Cohn:
Towards Robust and Privacy-preserving Text Representations. CoRR abs/1805.06093 (2018) - 2016
- [i1]Yitong Li, Trevor Cohn, Timothy Baldwin:
Learning Robust Representations of Text. CoRR abs/1609.06082 (2016)
Coauthor Index
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