Publications (by Year)

Preprints

Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment

Yang Liu, Yuanshun Yao, Jean-Francois Ton, Xiaoying Zhang, Ruocheng Guo, Hao Cheng, Yegor Klochkov, Muhammad Faaiz Taufiq and Hang Li

arXiv, under submission at Nature Machine Intelligence, 2023.

Rethinking Machine Unlearning for Large Language Models

Sijia Liu, Yuanshun Yao, Jinghan Jia, Stephen Casper, Nathalie Baracaldo, Peter Hase, Yuguang Yao, Chris Yuhao Liu, Xiaojun Xu, Hang Li, Kush R. Varshney, Mohit Bansal, Sanmi Koyejo, Yang Liu

arXiv, under full review at Nature Machine Intelligence, 2024.

2024

Predicting the replicability of social and behavioural science claims in COVID-19 preprints

Alexandru Marcoci et al.

Nature Human Behaviour, 2024.

Large Language Model Unlearning

Yuanshun Yao, Xiaojun Xu, Yang Liu

Neural Information Processing Systems (NeurIPS), 2024.

Large Language Model Unlearning via Embedding-Corrupted Prompts

Chris Yuhao Liu, Yaxuan Wang, Jeffrey Flanigan, Yang Liu

Neural Information Processing Systems (NeurIPS), 2024.

Mitigating Reward Overoptimization via Lightweight Uncertainty Estimation

Xiaoying Zhang, Jean-Francois Ton, Wei Shen, Hongning Wang, Yang Liu

Neural Information Processing Systems (NeurIPS), 2024.

Multi-LLM Debate: Framework, Principals, and Interventions

Andrew Estornell and Yang Liu

Neural Information Processing Systems (NeurIPS), 2024.

User-Creator Feature Dynamics in Recommender Systems with Dual Influence

Tao Lin, Kun Jin, Andrew Estornell, Xiaoying Zhang, Yiling Chen, Yang Liu

Neural Information Processing Systems (NeurIPS), 2024.

Learning the Optimal Policy for Balancing Multiple Short-Term and Long-Term Rewards

Qinwei Yang, Xueqing Liu, Yan Zeng, Ruocheng Guo, Yang Liu, Peng Wu

Neural Information Processing Systems (NeurIPS), 2024.

Achievable Fairness on Your Data With Utility Guarantees

Muhammad Faaiz Taufiq, Jean-Francois Ton, Yang Liu

Neural Information Processing Systems (NeurIPS), 2024.

Fairness without Harm: An Influence-Guided Active Sampling Approach

Jinlong Pang, Jialu Wang, Zhaowei Zhu, Yuanshun Yao, Chen Qian, Yang Liu

Neural Information Processing Systems (NeurIPS), 2024.

Performative Prediction with Bandit Feedback: Learning through Reparameterization

Yatong Chen, Wei Tang, Chien-Ju Ho, Yang Liu

International Conference on Machine Learning (ICML), 2024.

Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection

Chentao Cao, Zhun Zhong, Zhanke Zhou, Yang Liu, Tongliang Liu, Bo Han

International Conference on Machine Learning (ICML), 2024.

Post-hoc bias scoring is optimal for fair classification

Wenlong Chen, Yegor Klochkov, Yang Liu

International Conference on Learning Representations (ICLR), 2024.

Selected as a spotlight paper

Procedural Fairness Through Decoupling Objectionable Data Generating Components

Zeyu Tang, Jialu Wang, Yang Liu, Peter Spirtes, Kun Zhang

International Conference on Learning Representations (ICLR), 2024.

Selected as a spotlight paper

Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models

Zhaowei Zhu, Jialu Wang, Hao Cheng, Yang Liu

International Conference on Learning Representations (ICLR), 2024.

Fair Classifiers that Abstain without Harm

Tongxin Yin, Jean-François Ton, Ruocheng Guo, Yuanshun Yao, Mingyan Liu, Yang Liu

International Conference on Learning Representations (ICLR), 2024.

RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies

Hao Cheng, Qingsong Wen, Yang Liu, Liang Sun

International Conference on Learning Representations (ICLR), 2024.

Federated Learning with Local Openset Noisy Labels

Zonglin Di, Zhaowei Zhu, Xiaoxiao Li, Yang Liu

European Conference on Computer Vision (ECCV), 2024.

Providing Fair Recourse over Plausible Groups

Jayanth Yetukuri, Ian Hardy, Yevgeniy Vorobeychik, Berk Ustun and Yang Liu

AAAI Conference on Artificial Intelligence (AAAI), 2024.

Selected for an oral presentation

Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts

Kun Jin, Tongxin Yin, Zhongzhu Chen, Zeyu Sun, Xueru Zhang, Yang Liu, Mingyan Liu

AAAI Conference on Artificial Intelligence (AAAI), 2024.

Selected for an oral presentation

Fair Participation via Sequential Policies

Reilly Raab, Ross Boczar, Maryam Fazel and Yang Liu

AAAI Conference on Artificial Intelligence (AAAI), 2024.

FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning

Xinyuan Ji, Zhaowei Zhu, Wei Xi, Olga Gadyatskaya, Zilong Song, Yong Cai, Yang Liu

AAAI Conference on Artificial Intelligence (AAAI), 2024.

2023

Long-Term Fairness with Unknown Dynamics

Tongxin Yin*, Reilly Raab*, Mingyan Liu and Yang Liu

Neural Information Processing Systems (NeurIPS), 2023.

Best paper runner-up award at ICLR 2023 Workshop on Trustworthy and Reliable Large-Scale Machine Learning Models

Model Sparsity Can Simplify Machine Unlearning

Jinghan Jia, Jiancheng Liu, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma and Sijia Liu

Neural Information Processing Systems (NeurIPS), 2023.

https://github.com/OPTML-Group/Unlearn-Sparse

Selected as a spotlight paper

Uncertainty-Aware Instance Reweighting for Off-Policy Learning

Xiaoying Zhang, Junpu Chen, Hongning Wang, Hong Xie, Yang Liu, John C.S. Lui and Hang Li

Neural Information Processing Systems (NeurIPS), 2023.

To Aggregate or Not? Learning with Separate Noisy Labels

Jiaheng Wei, Zhaowei Zhu, Tianyi Luo, Ehsan Amid, Abhishek Kumar and Yang Liu

ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2023.

Debiasing Recommendation by Learning Identifiable Latent Confounders

Qing Zhang, Xiaoying Zhang, Yang Liu, Hongning Wang, Min Gao, Jiheng Zhang and Ruocheng Guo

ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2023.

Adaptive Adversarial Training Does Not Increase Recourse Costs

Ian Hardy, Jayanth Yetukuri and Yang Liu

AI Ethics and Society (AIES), 2023.

Towards User Guided Actionable Recourse

Jayanth Yetukuri, Ian Hardy and Yang Liu

AI Ethics and Society (AIES), 2023.

Weak Proxies are Sufficient and Preferrable for Fairness with Missing Sensitive Attributes

Zhaowei Zhu, Yuanshun Yao, Jiankai Sun, Hang Li and Yang Liu

International Conference on Machine Learning (ICML), 2023.

Identifiability of Label Noise Transition Matrix

Yang Liu, Hao Cheng and Kun Zhang

International Conference on Machine Learning (ICML), 2023.

Model Transferability with Responsive Decision Subjects

Yatong Chen, Zeyu Tang, Kun Zhang and Yang Liu

International Conference on Machine Learning (ICML), 2023.

Best paper award at ICML 2022 Workshop on New Frontiers in Adversarial Machine Learning

Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors

Zeyu Tang, Yatong Chen, Yang Liu and Kun Zhang

International Conference on Learning Representations (ICLR), 2023.

Mitigating Memorization of Noisy Labels via Regularization between Representations

Hao Cheng, Zhaowei Zhu, Xing Sun and Yang Liu

International Conference on Learning Representations (ICLR), 2023.

Distributionally Robust Post-hoc Classifiers under Prior Shifts

Jiaheng Wei, Harikrishna Narasimhan, Ehsan Amid, Wen-Sheng Chu, Yang Liu and Abhishek Kumar

International Conference on Learning Representations (ICLR), 2023.

Unfairness Despite Awareness: Group-Fair Classification with Strategic Agents

Andrew Estornell, Sanmay Das, Yang Liu and Yevgeniy Vorobeychik

ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT), 2023.

Best paper award at AAMAS 2022 Workshop on Learning with Strategic Agents

Measuring Valence and Stereotypical Biases in Text-to-Image Generation

Jialu Wang, Xinyue Gabby Liu, Zonglin Di, Yang Liu and Xin Eric Wang

Findings of Association for Computational Linguistics (ACL), 2023.

Incentivizing Recourse through Auditing in Strategic Classification

Andrew Estornell, Yatong Chen, Sanmay Das, Yang Liu and Yevgeniy Vorobeychik

International Joint Conference on Artificial Intelligence (IJCAI), 2023.

The Importance of Human-Labeled Data in the Era of LLMs

Yang Liu

International Joint Conference on Artificial Intelligence (IJCAI), 2023.

IJCAI 2023 Early Career Award talk

2022

Fairness Transferability Subject to Bounded Distribution Shift

Yatong Chen*, Reilly Raab*, Jialu Wang and Yang Liu

Neural Information Processing Systems (NeurIPS), 2022.

https://github.com/UCSC-REAL/Fairness_Transferability

Certifying Some Distributional Fairness with Subpopulation Decomposition

Mintong Kang, Linyi Li, Maurice Weber, Yang Liu, Ce Zhang, Bo Li

Neural Information Processing Systems (NeurIPS), 2022.

https://github.com/AI-secure/Certified-Fairness

Selected for a spotlight presentation.

Adaptive Data Debiasing Through Bounded Exploration

Yifan Yang, Yang Liu and Parinaz Naghizadeh

Neural Information Processing Systems (NeurIPS), 2022.

To Smooth or Not? When Label Smoothing Meets Noisy Labels

Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, and Yang Liu

International Conference on Machine Learning (ICML), 2022.

https://github.com/UCSC-REAL/negative-label-smoothing

Selected for a long talk, 2% acceptance.

Understanding Instance-Level Impact of Fairness Constraints

Jialu Wang, Xin Eric Wang and Yang Liu

International Conference on Machine Learning (ICML), 2022.

https://github.com/UCSC-REAL/FairInfl

Metric-Fair Classifier Derandomization

Jimmy Wu, Yatong Chen and Yang Liu

International Conference on Machine Learning (ICML), 2022.

Detecting Corrupted Labels Without Training a Model to Predict

Zhaowei Zhu, Zihao Dong, and Yang Liu

International Conference on Machine Learning (ICML), 2022.

https://github.com/UCSC-REAL/SimiRep

Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features

Zhaowei Zhu, Jialu Wang and Yang Liu

International Conference on Machine Learning (ICML), 2022.

https://github.com/UCSC-REAL/BeyondImages

Estimating Instance-dependent Label-noise Transition Matrix using a Deep Neural Network

Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, and Tongliang Liu

International Conference on Machine Learning (ICML), 2022.

https://github.com/ShuoYang-1998/BLTM

Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Jiaheng Wei*, Zhaowei Zhu*, Hao Cheng, Tongliang Liu, Gang Niu, and Yang Liu

International Conference on Learning Representations (ICLR), 2022.

http://www.noisylabels.com

The Rich Get Richer: Disparate Impact of Semi-Supervised Learning

Zhaowei Zhu, Tianyi Luo and Yang Liu

International Conference on Learning Representations (ICLR), 2022.

https://github.com/UCSC-REAL/Disparate-SSL

Forecasting the publication and citation outcomes of Covid-19 preprints

Michael Gordon, Michael Bishop, Yiling Chen, Anna Dreber, Brandon Goldfedder, Felix Holzmeister, Magnus Johannesson, Yang Liu, Louisa Tran, Charles Twardy, Juntao Wang and Thomas Pfeiffer

Royal Society Open Science (RSOS), 2022.

DuelGAN: A Duel Between Two Discriminators Stabilizes the GAN Training

Jiaheng Wei, Minghao Liu, Jiahao Luo, Andrew Zhu, James Davis, Yang Liu

European Conference on Computer Vision (ECCV), 2022.

https://github.com/UCSC-REAL/DuelGAN

Assessing Multilingual Fairness in Pre-trained Multimodal Representations

Jialu Wang, Yang Liu and Xin Eric Wang

Findings of Association for Computational Linguistics (ACL), 2022.

Interpretable Research Replication Prediction via Variational Contextual Consistency Sentence Masking

Tianyi Luo, Rui Meng, Xin Eric Wang and Yang Liu

Association for Computational Linguistics (ACL), 2022.

Fair Classification with Instance-dependent Label Noise

Songhua Wu, Mingming Gong, Bo Han, Yang Liu and Tongliang Liu

Causal Learning and Reasoning (CLeaR), 2022.

Machine Truth Serum: a Surprisingly Popular Approach to Improving Ensemble Methods

Tianyi Luo and Yang Liu

Machine Learning, 2022.

Also appeared in the journal track of AAAI Conference on Artificial Intelligence (AAAI) 2023.

2021

Linear Classifiers that Encourage Constructive Adaptation

Yatong Chen, Jialu Wang and Yang Liu

Transactions on Machine Learning Research (TMLR), 2023.

Best paper award at ICML 2021 Workshop on Algorithmic Recourse

Unintended Selection: Persistent Qualification Rate Disparities and Interventions

Reilly Raab and Yang Liu

Neural Information Processing Systems (NeurIPS), 2021.

Selected for a spotlight presentation, 3% acceptance.

Policy Learning Using Weak Supervision

Jingkang Wang*, Hongyi Guo*, Zhaowei Zhu* and Yang Liu

Neural Information Processing Systems (NeurIPS), 2021.

https://github.com/wangjksjtu/PeerPL

Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial

Yang Liu and Jialu Wang

Neural Information Processing Systems (NeurIPS), 2021.

https://github.com/UCSC-REAL/CanLessBeMore

Bandit Learning with Delayed Impact of Actions

Wei Tang, Chien-Ju Ho and Yang Liu

Neural Information Processing Systems (NeurIPS), 2021.

Understanding Instance-Level Label Noise: Disparate Impacts and Treatments

Yang Liu

International Conference on Machine Learning (ICML), 2021.

Selected for a long talk, 2% acceptance.

Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels

Zhaowei Zhu, Yiwen Song and Yang Liu

International Conference on Machine Learning (ICML), 2021.

https://github.com/UCSC-REAL/HOC

Best paper award at IJCAI 2021 workshop on Weakly Supervised Representation Learning.

When Optimizing f-divergence is Robust with Label Noise

Jiaheng Wei and Yang Liu

International Conference on Learning Representations (ICLR), 2021.

https://github.com/UCSC-REAL/Robust-f-divergence-measures

Learning with Instance-Dependent Label Noise: A Sample Sieve Approach

Hao Cheng*, Zhaowei Zhu*, Xingyu Li, Yifei Gong, Xing Sun and Yang Liu

International Conference on Learning Representations (ICLR), 2021.

https://github.com/UCSC-REAL/cores

A Second-Order Approach to Learning with Instance-Dependent Label Noise

Zhaowei Zhu, Tongliang Liu and Yang Liu

Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

https://github.com/UCSC-REAL/CAL

Selected for an oral presentation.

Fair Classification with Group-Dependent Label Noise

Jialu Wang, Yang Liu (corresponding) and Caleb Levy

ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT), 2021.

https://github.com/UCSC-REAL/fair-classification-with-noisy-labels

Linear Models are Robust Optimal Under Strategic Behavior

Wei Tang, Chien-Ju Ho and Yang Liu

International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.

Sample Elicitation

Jiaheng Wei*, Zuyue Fu*, Yang Liu (corresponding), Xingyu Li, Zhuoran Yang and Zhaoran Wang

International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.

https://github.com/UCSC-REAL/Credible-sample-elicitation

Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search

Jialu Wang and Yang Liu and Xin Eric Wang

Empirical Method in Natural Language Processing (EMNLP), 2021.

Selected for an oral presentation.

Forecast Aggregation via Peer Prediction

Juntao Wang, Yang Liu and Yiling Chen

AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2021.

Federated Bandit: A Gossiping Approach

Zhaowei Zhu, Jingxuan Zhu, Ji Liu and Yang Liu

ACM International Conference on Measurement and Modeling of Computer Systems (ACM SIGMETRICS), 2021.

2020

How Do Fair Decisions Fare in Long-term Qualification?

Xueru Zhang, Ruibo Tu, Yang Liu, Mingyan Liu, Hedvig Kjellström, Kun Zhang and Cheng Zhang

Neural Information Processing Systems (NeurIPS), 2020.

Optimal Query Complexity of Secure Stochastic Convex Optimization

Wei Tang, Chien-Ju Ho and Yang Liu

Neural Information Processing Systems (NeurIPS), 2020.

Learning Strategy-Aware Linear Classifiers

Yiling Chen, Yang Liu and Chara Podimata

Neural Information Processing Systems (NeurIPS), 2020.

Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates

Yang Liu and Hongyi Guo

International Conference on Machine Learning (ICML), 2020.

https://github.com/UCSC-REAL/PeerLoss

Online Learning Using Only Peer Prediction

Yang Liu and David P. Helmbold

International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.

Research Replication Prediction Using Weakly Supervised Learning

Tianyi Luo, Xingyu Li, Hainan Wang and Yang Liu

Findings at Empirical Method in Natural Language Processing (EMNLP), 2020.

https://github.com/pkuluotianyi/PeerRRP

Are replication rates the same across academic fields? Community forecasts from the DARPA SCORE program

(alphabetical after MG and DV) Michael Gordon, Domenico Viganola, Michael Bishop, Yiling Chen, Anna Dreber, Brandon Goldfedder, Felix Holzmeister, Magnus Johannesson, Yang Liu, Charles Twardy, Juntao Wang and Thomas Pfeiffer

Royal Society Open Science (RSOS), 2020.

How do fairness definitions fare? Testing public attitudes towards three algorithmic definition of fairness in loan allocations

Nripsuta Saxena*, Karen Huang, Evan DeFilippis, Goran Radanovic, David Parkes and Yang Liu*

Artificial Intelligence Journal (AIJ), 2020.

This journal version subsumes our AI Ethics and Society'19 paper.

Reinforcement Learning with Perturbed Rewards

Jingkang Wang, Yang Liu and Bo Li

AAAI Conference on Artificial Intelligence (AAAI), 2020.

https://github.com/wangjksjtu/rl-perturbed-reward

Surrogate Scoring Rules

Yang Liu*, Juntao Wang* and Yiling Chen

ACM Conference on Economics and Computation (ACM EC), 2020.

Extended version appeared at ACM Transactions on Economics and Computation (ACM TEAC), 2022

Differentially Private Contextual Dynamic Pricing

Wei Tang, Chien-Ju Ho and Yang Liu

International Conference on Autonomous Agents and Multiagent Systems (AAMAS), full paper, 2020.

2019

Fairness without Harm: Decoupled Classifiers with Preference Guarantees

Berk Ustun, Yang Liu and David Parkes

International Conference on Machine Learning (ICML), 2019.

https://github.com/ustunb/dcptree

Actionable Recourse in Linear Classification

Berk Ustun, Alexander Spangher and Yang Liu

ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT), 2019.

https://github.com/ustunb/actionable-recourse

How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness

Nripsuta Saxena*, Karen Huang, Evan DeFilippis, Goran Radanovic, David Parkes and Yang Liu*

AI Ethics and Society (AIES), 2019.

Bayesian Fairness

Christos Dimitrakakis, Yang Liu, David Parkes and Goran Radanovic

AAAI Conference on Artificial Intelligence (AAAI), 2019.

Randomized Wagering Mechanism

Yiling Chen, Yang Liu and Juntao Wang

AAAI Conference on Artificial Intelligence (AAAI), 2019.

https://github.com/yangl0320/wager_exp

2018

Inference aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing

Zehong Hu, Yitao Liang, Jie Zhang, Zhao Li, and Yang Liu*

Neural Information Processing Systems (NeurIPS), 2018.

https://github.com/vascohu/RLPeerPrediction

Active Information Acquisition for Linear Optimization

Shuran Zheng, Bo Waggoner, Yang Liu and Yiling Chen

Uncertainty in Artificial Intelligence (UAI), 2018.

Incentivizing High Quality User Contributions: New Arm Generation in Bandit Learning

Yang Liu and Chien-Ju Ho

AAAI Conference on Artificial Intelligence (AAAI), 2018.

2017

Machine Learning aided Peer Prediction

Yang Liu and Yiling Chen

ACM Conference on Economics and Computation (ACM EC), 2017.

Fair Optimal Stopping Policy for Matching with Mediator

Yang Liu

Uncertainty in Artificial Intelligence (UAI), 2017.

Crowd Learning: Improving Online Decision Making Using Crowdsourced Data

Yang Liu and Mingyan Liu

International Joint Conference on Artificial Intelligence (IJCAI), 2017.

Sequential Peer Prediction: Learning to Elicit Effort using Posted Prices

Yang Liu and Yiling Chen

AAAI Conference on Artificial Intelligence (AAAI), 2017.

Calibrated Fairness in Bandits

Yang Liu, Goran Radanovic, Christos Dimitrakakis, Debmalya Mandal and David Parkes

Fairness, Accountability and Transparency in Machine Learning (FAT-ML), 2017.

2016

A Bandit Framework for Strategic Regression

Yang Liu and Yiling Chen

Neural Information Processing Systems (NeurIPS), 2016.

Learning to Incentivize: Eliciting Effort via Output Agreement

Yang Liu and Yiling Chen

International Joint Conference on Artificial Intelligence (IJCAI), 2016.

Finding One's Best Crowd: Online Learning By Exploiting Source Similarity

Yang Liu and Mingyan Liu

AAAI Conference on Artificial Intelligence (AAAI), 2016.

A Note on the Unification of Adaptive Online Learning

Wenwu He, James T. Kwok, Ji Zhu and Yang Liu

IEEE Trans. on Neural Networks and Learning Systems (TNNLS), 2016.

2015

An Online Learning Approach to Improving the Quality of Crowdsourcing

Yang Liu, Mingyan Liu

ACM International Conference on Measurement and Modeling of Computer Systems (ACM SIGMETRICS), 2015.

Extended version appeared at IEEE/ACM Transaction on Networkings, 2018.

Cloudy with a Chance of Breach: Forecasting Cyber Security Incidents

Yang Liu, Armin Sarabi, Jing Zhang, Parinaz Ardabili, Manish Karir, Michael Bailey and Mingyan Liu

USENIX Security, 2015.

An Online Approach to Dynamic Channel Access and Transmission Scheduling

Yang Liu, Mingyan Liu

ACM International Symposium on Mobile Ad Hoc Networking and Computing (ACM MobiHoc), 2015.

Extended version appeared at at Handbook of Cognitive Radio, Springer.

2014

Detecting Hidden Propagation Structure and Its Application to Analyzing Phishing

Yang Liu, Mingyan Liu

ACM/IEEE Data Science and Advanced Analytics (DSAA), 2014.

Received best application paper award.

Sufficient Conditions on Optimality of Myopic Sensing in Opportunistic Channel Access: A Unifying Framework

Yang Liu, Mingyan Liu and Sahand Haji Ali Ahmad

IEEE Transaction on Information Theory (TIT), 2014.

Miscellaneous

Distributed Belief Averaging Using Sequential Observations

Kaiqing Zhang, Yang Liu , Ji Liu, Tamer Başar and Mingyan Liu

Automatica, 2020.

From Patching Delays to Infection Symptoms: Using Risk Profiles for an Early Discovery of Vulnerabilities Exploited in the Wild

Chaowei Xiao, Armin Sarabi, Yang Liu , Bo Li, Mingyan Liu and Tudor Dumitras

USENIX Security, 2018.

Risky Business: Fine-grained Data Breach Prediction Using Business Profiles

Armin Sarabi, Parinaz Naghizadeh, Yang Liu and Mingyan Liu

Journal of Cybersecurity, 2016.

Earlier version appeared at WEIS'15.

To Stay Or To Switch: Multiuser Dynamic Channel Access

Yang Liu, Mingyan Liu

IEEE INFOCOM, 2013.

Extended version appeared at IEEE Transaction on Mobile Computing, 2014.

Is Diversity Gain Worth the Pain:a Delay Comparison between Opportunistic Multi-channel MAC and Single-channel MAC

Yang Liu, Mingyan Liu, Jing Deng

IEEE INFOCOM, 2012.

Extended version appeared at IEEE Journal on Selected Areas in Communications, 2013.