79 citations · 158 across the 15 of their papers we have counts for
17 papers
Consideration of Control-Loop Interaction in Transient Stability of Grid-Following Inverters using Bandwidth Separation Method
Yifan Zhang, Yunjie Gu, Yue Zhu +3
Grid-following inverters have been widely adopted as a grid interface for renewable energy, and ensuring their small-signal and large-signal stability is critical to modern power s…
Descriptor State Space Modeling of Power Systems
Yitong Li, Timothy C. Green, Yunjie Gu
State space is widely used for modeling power systems and analyzing their dynamics but it is limited to representing causal and proper systems in which the number of zeros does not…
Whole-System First-Swing Stability of Inverter-Based Inertia-Free Power Systems
Yitong Li, Yunjie Gu
The emphasis on inertia for system stability has been a long-held tradition in conventional grids. The fast and flexible controllability of inverters opens up new possibilities. Th…
fairlib: A Unified Framework for Assessing and Improving Classification Fairness
Xudong Han, Aili Shen, Yitong Li +3
This paper presents fairlib, an open-source framework for assessing and improving classification fairness. It provides a systematic framework for quickly reproducing existing basel…
Impedance-based Root-cause Analysis: Comparative Study of Impedance Models and Calculation of Eigenvalue Sensitivity
Yue Zhu, Yunjie Gu, Yitong Li +1
Impedance models of power systems are useful when state-space models of apparatus such as inverter-based resources (IBRs) have not been made available and instead only black-box im…
Uncertainty-Aware Balancing for Multilingual and Multi-Domain Neural Machine Translation Training
Minghao Wu, Yitong Li, Meng Zhang +3
Learning multilingual and multi-domain translation model is challenging as the heterogeneous and imbalanced data make the model converge inconsistently over different corpora in re…
DocTer: Documentation Guided Fuzzing for Testing Deep Learning API Functions
Danning Xie, Yitong Li, Mijung Kim +4
Input constraints are useful for many software development tasks. For example, input constraints of a function enable the generation of valid inputs, i.e., inputs that follow these…
The Intrinsic Communication in Power Systems: A New Perspective to Understand Synchronization Stability
Yitong Li, Timothy C. Green, Yunjie Gu
Synchronization is an essential element in three-phase ac electric power systems. The large-scale integration of converter-interfaced resources leads to the power grid transformati…
Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness
Lingjuan Lyu, Xuanli He, Yitong Li
It has been demonstrated that hidden representation learned by a deep model can encode private information of the input, hence can be exploited to recover such information with rea…
How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning
Lingjuan Lyu, Yitong Li, Karthik Nandakumar +2
This paper firstly considers the research problem of fairness in collaborative deep learning, while ensuring privacy. A novel reputation system is proposed through digital tokens a…
Towards Differentially Private Text Representations
Lingjuan Lyu, Yitong Li, Xuanli He +1
Most deep learning frameworks require users to pool their local data or model updates to a trusted server to train or maintain a global model. The assumption of a trusted server wh…
Semi-supervised Stochastic Multi-Domain Learning using Variational Inference
Yitong Li, Timothy Baldwin, Trevor Cohn
Supervised models of NLP rely on large collections of text which closely resemble the intended testing setting. Unfortunately matching text is often not available in sufficient qua…
Towards Fair and Privacy-Preserving Federated Deep Models
Lingjuan Lyu, Jiangshan Yu, Karthik Nandakumar +5
The current standalone deep learning framework tends to result in overfitting and low utility. This problem can be addressed by either a centralized framework that deploys a centra…
Sequential Attention GAN for Interactive Image Editing
Yu Cheng, Zhe Gan, Yitong Li +2
Most existing text-to-image synthesis tasks are static single-turn generation, based on pre-defined textual descriptions of images. To explore more practical and interactive real-l…
Towards Robust and Privacy-preserving Text Representations
Yitong Li, Timothy Baldwin, Trevor Cohn
Written text often provides sufficient clues to identify the author, their gender, age, and other important attributes. Consequently, the authorship of training and evaluation corp…
What's in a Domain? Learning Domain-Robust Text Representations using Adversarial Training
Yitong Li, Timothy Baldwin, Trevor Cohn
Most real world language problems require learning from heterogenous corpora, raising the problem of learning robust models which generalise well to both similar (in domain) and di…
Learning Robust Representations of Text
Yitong Li, Trevor Cohn, Timothy Baldwin
Deep neural networks have achieved remarkable results across many language processing tasks, however these methods are highly sensitive to noise and adversarial attacks. We present…