By Professor Lina Yao

Conference Papers

Ruan W; Yao L; Sheng QZ; Falkner NJG; Li X, 2014, 'TagTrack: Device-free localization and tracking using passive RFID tags', in Mobiquitous 2014 11th International Conference on Mobile and Ubiquitous Systems Computing Networking and Services, pp. 80 - 89, http://dx.doi.org/10.4108/icst.mobiquitous.2014.258004

Yao L; Sheng QZ; Falkner NJG; Ngu AHH, 2014, 'ThingsNavi: Finding most-related things via multi-dimensional modeling of human-thing interactions', in Mobiquitous 2014 11th International Conference on Mobile and Ubiquitous Systems Computing Networking and Services, pp. 20 - 29, http://dx.doi.org/10.4108/icst.mobiquitous.2014.258007

Yao L; Sheng QZ; Gao BJ; Ngu AHH; Li X, 2013, 'A model for discovering correlations of ubiquitous things', in Proceedings IEEE International Conference on Data Mining Icdm, pp. 1253 - 1258, http://dx.doi.org/10.1109/ICDM.2013.87

Yao L; Sheng QZ, 2013, 'Correlation discovery in web of things', in Www 2013 Companion Proceedings of the 22nd International Conference on World Wide Web, pp. 215 - 216, http://dx.doi.org/10.1145/2487788.2487898

Yao L; Mathew SS; Ruta M, 2013, 'Preface', in Procedia Computer Science, pp. 1129, http://dx.doi.org/10.1016/j.procs.2013.06.159

Yao L; Sheng QZ; Segev A; Yu J, 2013, 'Recommending web services via combining collaborative filtering with content-based features', in Proceedings IEEE 20th International Conference on Web Services Icws 2013, pp. 42 - 49, http://dx.doi.org/10.1109/ICWS.2013.16

Yao L; Sheng QZ, 2012, 'A tag-centric discriminative model for web objects classification', in ACM International Conference Proceeding Series, pp. 2247 - 2250, http://dx.doi.org/10.1145/2396761.2398612

Yao L; Sheng QZ, 2012, 'Exploiting latent relevance for relational learning of ubiquitous things', in ACM International Conference Proceeding Series, pp. 1547 - 1551, http://dx.doi.org/10.1145/2396761.2398470

Maamar Z; Faci N; Sheng QZ; Yao L, 2012, 'Towards a user-centric social approach to web services composition, execution, and monitoring', in Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, pp. 72 - 86, http://dx.doi.org/10.1007/978-3-642-35063-4_6

Yao L, 2012, 'A propagation model for integrating web of things and social networks', in Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, pp. 233 - 238, http://dx.doi.org/10.1007/978-3-642-31875-7_28

Wu Y; Sheng QZ; Ranasinghe D; Yao L, 2012, 'PeerTrack: A platform for tracking and tracing objects in large-scale traceability networks', in ACM International Conference Proceeding Series, pp. 586 - 589, http://dx.doi.org/10.1145/2247596.2247672

Zheng Z; Wang H; Yao L, 2012, 'An Artificial Bee Colony Optimization algorithm for multicast routing', in International Conference on Advanced Communication Technology Icact, pp. 168 - 172

Yao L; Sheng QZ, 2011, 'Particle filtering based availability prediction for web services', in Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, pp. 566 - 573, http://dx.doi.org/10.1007/978-3-642-25535-9_42

Pan S; Hu R; Long G; Jiang J; Yao L; Zhang C, 'Adversarially Regularized Graph Autoencoder for Graph Embedding', in Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18, International Joint Conferences on Artificial Intelligence Organization, pp. 2609 - 2615, http://dx.doi.org/10.24963/ijcai.2018/362

Preprints

Lou H; Paik H; Hu W; Yao L, 2024, StyleSpeech: Parameter-efficient Fine Tuning for Pre-trained Controllable Text-to-Speech, http://dx.doi.org/10.48550/arxiv.2408.14713

Liu Y; Sheng QZ; Yao L, 2024, Modeling Pedestrian Intrinsic Uncertainty for Multimodal Stochastic Trajectory Prediction via Energy Plan Denoising, http://dx.doi.org/10.48550/arxiv.2405.07164

Liu Y; Wang R; Cao Y; Sheng QZ; Yao L, 2024, Multi-agent Traffic Prediction via Denoised Endpoint Distribution, http://dx.doi.org/10.48550/arxiv.2405.07041

Wang H; Lu H; Yao L; Gong D, 2024, Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning, http://dx.doi.org/10.48550/arxiv.2403.18886

Huang H; Chang X; Hu W; Yao L, 2024, MatchNAS: Optimizing Edge AI in Sparse-Label Data Contexts via Automating Deep Neural Network Porting for Mobile Deployment, http://dx.doi.org/10.48550/arxiv.2402.13525

Chen X; Wang S; McAuley J; Jannach D; Yao L, 2023, On the Opportunities and Challenges of Offline Reinforcement Learning for Recommender Systems, http://dx.doi.org/10.48550/arxiv.2308.11336

Liu Y; Cui G; Luo J; Chang X; Yao L, 2023, Two-stream Multi-level Dynamic Point Transformer for Two-person Interaction Recognition, http://dx.doi.org/10.48550/arxiv.2307.11973

Wang S; Chen X; Jannach D; Yao L, 2023, Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning, http://dx.doi.org/10.48550/arxiv.2304.07920

Wang S; Chen X; Sheng QZ; Zhang Y; Yao L, 2023, Causal Disentangled Variational Auto-Encoder for Preference Understanding in Recommendation, http://dx.doi.org/10.48550/arxiv.2304.07922

Li Z; Chen Y; Wang X; Yao L; Xu G, 2023, Multi-view GCN for Loan Default Risk Prediction, http://dx.doi.org/10.21203/rs.3.rs-2754272/v1

Liu Y; Ye Z; Wang R; Li B; Sheng QZ; Yao L, 2023, Uncertainty-Aware Pedestrian Trajectory Prediction via Distributional Diffusion, http://dx.doi.org/10.48550/arxiv.2303.08367

Cao Y; Yao L; Pan L; Sheng QZ; Chang X, 2023, Guided Image-to-Image Translation by Discriminator-Generator Communication, http://dx.doi.org/10.48550/arxiv.2303.03598

Li C; Bai L; Yao L; Waller ST; Liu W, 2022, A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications, http://dx.doi.org/10.48550/arxiv.2210.14524

Chen X; Wang S; Yao L; Qi L; Li Y, 2022, Intrinsically Motivated Reinforcement Learning based Recommendation with Counterfactual Data Augmentation, http://dx.doi.org/10.48550/arxiv.2209.08228

Wang S; Chen X; Yao L; Cripps S; McAuley J, 2022, Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning based Recommendation, http://dx.doi.org/10.48550/arxiv.2208.05142

Soltani B; Haghighi V; Mahmood A; Sheng QZ; Yao L, 2022, A Survey on Participant Selection for Federated Learning in Mobile Networks, http://dx.doi.org/10.48550/arxiv.2207.03681

Li C; Bai L; Liu W; Yao L; Waller ST, 2022, Unsupervised Knowledge Adaptation for Passenger Demand Forecasting, http://dx.doi.org/10.48550/arxiv.2206.04053

Li Y; Liu Z; Yao L; Monaghan JJM; McAlpine D, 2022, Disentangled and Side-aware Unsupervised Domain Adaptation for Cross-dataset Subjective Tinnitus Diagnosis, http://dx.doi.org/10.48550/arxiv.2205.03230

Li Y; Liu Z; Yao L; Lucas M; Monaghan JJM; Zhang Y, 2022, Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus, http://dx.doi.org/10.48550/arxiv.2205.03231

Wang S; Cao Y; Chen X; Yao L; Wang X; Sheng QZ, 2021, Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems, http://dx.doi.org/10.48550/arxiv.2112.00973

Liu Z; Li Y; Yao L; McAuley J; Dixon S, 2021, Rethink, Revisit, Revise: A Spiral Reinforced Self-Revised Network for Zero-Shot Learning, http://arxiv.org/abs/2112.00410v1

Chen X; Yao L; Wang X; McAuley J, 2021, Locality-Sensitive Experience Replay for Online Recommendation, http://dx.doi.org/10.48550/arxiv.2110.10850

Chen X; Yao L; Wang X; Sun A; Zhang W; Sheng QZ, 2021, Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference, http://dx.doi.org/10.48550/arxiv.2105.00822

Bouguettaya A; Sheng QZ; Benatallah B; Neiat AG; Mistry S; Ghose A; Nepal S; Yao L, 2021, An Internet of Things Service Roadmap, http://dx.doi.org/10.48550/arxiv.2103.03043

Li C; Bai L; Liu W; Yao L; Waller ST, 2020, Knowledge Adaption for Demand Prediction based on Multi-task Memory Neural Network, http://dx.doi.org/10.48550/arxiv.2009.05777

Altulyan M; Yao L; Wang X; Huang C; Kanhere SS; Sheng QZ, 2020, Recommender Systems for the Internet of Things: A Survey, http://arxiv.org/abs/2007.06758v1

Liu Z; Yao L; Bai L; Wang X; Wang C, 2020, Spectrum-Guided Adversarial Disparity Learning, http://dx.doi.org/10.1145/3394486.3403054

Chen X; Yao L; Zhou T; Dong J; Zhang Y, 2020, Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images, http://dx.doi.org/10.48550/arxiv.2006.13276

Wang X; Yao L; Wang X; Nie F, 2020, NP-PROV: Neural Processes with Position-Relevant-Only Variances, http://dx.doi.org/10.48550/arxiv.2007.00767

Cao Y; Chen X; Yao L; Wang X; Zhang WE, 2020, Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems, http://dx.doi.org/10.48550/arxiv.2006.07934

Liu Z; Li Y; Yao L; Wang X; Nie F, 2020, Agglomerative Neural Networks for Multi-view Clustering, http://arxiv.org/abs/2005.05556v1

Tran DH; Sheng QZ; Zhang WE; Hamad SA; Zaib M; Tran NH; Yao L; Khoa NLD, 2020, Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems, http://dx.doi.org/10.48550/arxiv.2004.13245

Liu Z; Yao L; Wang X; Bai L; An J, 2020, Are You A Risk Taker? Adversarial Learning of Asymmetric Cross-Domain Alignment for Risk Tolerance Prediction, http://dx.doi.org/10.1109/IJCNN48605.2020.9207111

Chen X; Huang C; Yao L; Wang X; Liu W; Zhang W, 2020, Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation, http://dx.doi.org/10.48550/arxiv.2004.08068

Xu W; Zhang X; Yao L; Xue W; Wei B, 2020, A Multi-view CNN-based Acoustic Classification System for Automatic Animal Species Identification, http://dx.doi.org/10.48550/arxiv.2002.09821

Zhang X; Chen X; Dong M; Liu H; Ge C; Yao L, 2019, Multi-task Generative Adversarial Learning on Geometrical Shape Reconstruction from EEG Brain Signals, http://dx.doi.org/10.48550/arxiv.1907.13351

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