PhD in Computer Science and Engineering, UNSW Sydney.
Engineering
Computer Science and Engineering
Dr Siyu Wang is a Postdoctoral Fellow at UNSW Sydney, working with Professor Lina Yao. Her research explores how machine learning systems can reason about cause and effect and make reliable decisions, with applications in reinforcement learning and recommender systems.
She completed her PhD in Computer Science and Engineering at UNSW under the supervision of Professor Lina Yao. Previously, she held a postdoctoral position at Macquarie University, working with Professor Michael Sheng.
Wang S; Chen X; McAuley J; Cripps S; Yao L, 2025, 'Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning-Based Recommendation', IEEE Transactions on Neural Networks and Learning Systems, 36, pp. 1044 - 1055, http://dx.doi.org/10.1109/TNNLS.2023.3329808
Journal articles | 2025
Wang S; Chen X; Yao L, 2025, 'Retentive Decision Transformer with Adaptive Masking for Reinforcement Learning-Based Recommendation Systems', ACM Transactions on Intelligent Systems and Technology, 16, http://dx.doi.org/10.1145/3719208
Journal articles | 2024
Chen X; Wang S; McAuley J; Jannach D; Yao L, 2024, 'On the Opportunities and Challenges of Offline Reinforcement Learning for Recommender Systems', ACM Transactions on Information Systems, 42, http://dx.doi.org/10.1145/3661996
Journal articles | 2023
Chen X; Wang S; Qi L; Li Y; Yao L, 2023, 'Intrinsically motivated reinforcement learning based recommendation with counterfactual data augmentation', World Wide Web, 26, pp. 3253 - 3274, http://dx.doi.org/10.1007/s11280-023-01187-7
Journal articles | 2022
Wang S; Cao Y; Chen X; Yao L; Wang X; Sheng QZ, 2022, 'Adversarial Robustness of Deep Reinforcement Learning Based Dynamic Recommender Systems', Frontiers in Big Data, 5, http://dx.doi.org/10.3389/fdata.2022.822783
Conference Papers | 2026
Chen X; Wang S; Yao L, 2026, 'Advantage-Conditioned Flow Policy for Offline Reinforcement Learning in Recommendation', in SIGIR 2026 Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 179 - 189, http://dx.doi.org/10.1145/3805712.3809626
Conference Papers | 2025
Chen X; Wang S; Yao L, 2025, 'Energy-Guided Diffusion Sampling for Long-Term User Behavior Prediction in Reinforcement Learning-based Recommendation', in Cikm 2025 Proceedings of the 34th ACM International Conference on Information and Knowledge Management, pp. 322 - 331, http://dx.doi.org/10.1145/3746252.3761076
Conference Papers | 2025
Chen X; Wang S; Yao L, 2025, 'Maximum In-Support Return Modeling for Dynamic Recommendation with Language Model Prior', in Cikm 2025 Proceedings of the 34th ACM International Conference on Information and Knowledge Management, pp. 332 - 341, http://dx.doi.org/10.1145/3746252.3761336
Preprints | 2025
Chen X; Wang S; Yao L, 2025, Energy-Guided Diffusion Sampling for Long-Term User Behavior Prediction in Reinforcement Learning-based Recommendation, http://dx.doi.org/10.48550/arxiv.2510.12815
Chen X; Wang S; Yu T; Yao L, 2025, 'Diffusion Policies for Risk-Averse Behavior Modeling in Offline Reinforcement Learning', in IEEE International Conference on Intelligent Robots and Systems, pp. 567 - 574, http://dx.doi.org/10.1109/IROS60139.2025.11245819
Wang S; Chen X; Yao L, 2025, 'Policy-Guided Causal State Representation for Offline Reinforcement Learning Recommendation', in Www 2025 Proceedings of the ACM Web Conference, pp. 402 - 412, http://dx.doi.org/10.1145/3696410.3714562
Chen X; Wang S; Yao L, 2024, 'Maximum-Entropy Regularized Decision Transformer with Reward Relabelling for Dynamic Recommendation', in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 376 - 384, http://dx.doi.org/10.1145/3637528.3671750
Preprints | 2024
Chen X; Wang S; Yao L, 2024, Maximum-Entropy Regularized Decision Transformer with Reward Relabelling for Dynamic Recommendation, http://dx.doi.org/10.48550/arxiv.2406.00725
Conference Papers | 2024
Wang S; Chen X; Yao L, 2024, 'On Causally Disentangled State Representation Learning for Reinforcement Learning based Recommender Systems', in International Conference on Information and Knowledge Management Proceedings, pp. 2390 - 2399, http://dx.doi.org/10.1145/3627673.3679674
Preprints | 2024
Wang S; Chen X; Yao L, 2024, On Causally Disentangled State Representation Learning for Reinforcement Learning based Recommender Systems, http://dx.doi.org/10.48550/arxiv.2407.13091
Preprints | 2024
Wang S; Chen X; Yao L, 2024, Retentive Decision Transformer with Adaptive Masking for Reinforcement Learning based Recommendation Systems, http://dx.doi.org/10.48550/arxiv.2403.17634
Preprints | 2023
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
Conference Papers | 2023
Wang S; Chen X; Jannach D; Yao L, 2023, 'Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning', in SIGIR 2023 Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1599 - 1608, http://dx.doi.org/10.1145/3539618.3591648
Wang S; Chen X; Sheng QZ; Zhang Y; Yao L, 2023, 'Causal Disentangled Variational Auto-Encoder for Preference Understanding in Recommendation', in SIGIR 2023 Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1874 - 1878, http://dx.doi.org/10.1145/3539618.3591961
Preprints | 2023
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
Preprints | 2023
Wang S; Chen X; Yao L; Cripps S; McAuley J, 2023, Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning based Recommendation, http://dx.doi.org/10.48550/arxiv.2208.05142
Preprints | 2022
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
Conference Papers | 2022
Chen X; Yao L; Chang X; Wang S, 2022, 'Empowerment-driven Policy Gradient Learning with Counterfactual Augmentation in Recommender Systems', in Proceedings IEEE International Conference on Data Mining Icdm, pp. 885 - 890, http://dx.doi.org/10.1109/ICDM54844.2022.00102
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
