a list of our achievements
(Deep) Reinforcement Learning
- Hongyu Zang, Xin Li, Leiji Zhang, Yang Liu, Baigui Sun, Riashat Islam, Remi Taqchet des Combes, Romain Laroche. "Understanding and Addressing the Pitfalls of Bisimulation-based Representations in Offline Reinforcement Learning" NeurIPS 2023
- Xin Li, Haojie Lei, Li Zhang, Mingzhong Wang. "Differentiable Logic Policy for Interpretable Deep Reinforcement Learning: A Study from an Optimization Perspective" TPAMI 2023
- Riashat Islam, Manan Tomar, Alex Lamb, Yonathan Efroni, Hongyu Zang, Aniket Didolkar, Dipendra Misra, Xin Li, Harm van Seijen, Remi Tachet des Combes, John Langford. "Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information" ICML 2023. (CCF A)
- Hongyu Zang, Xin Li, Jie Yu, Chen Liu, Riashat Islam, Remi Tachet des Combes, Romain Laroche. "Behavior Prior Representation learning for Offline Reinforcement Learning" ICLR 2023
- Riashat Islam*, Hongyu Zang*, Manan Tomar, Aniket Didolkar, Md Mofijul Islam, Samin Yeasar Arnob, Tariq Iqbal, Xin Li, Anirudh Goyal, Nicolas Heess, Alex Lamb. "Representation Learning in Deep RL via Discrete Information Bottleneck" AISTATS 2023
- Riashat Islam, Hongyu Zang, Anirudh Goyal, Alex Lamb, Kenji Kawaguchi, Xin Li, Romain Laroche, Yoshua Bengio, Remi Tachet des Combes. "Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning" NeurIPS 2022. (CCF A)
- Hongyu Zang, Xin Li, Mingzhong Wang. "SimSR: Simple Distance-based State Representation for Deep Reinforcement Learning" AAAI 2022. (CCF A)
- Li Zhang, Xin Li, etc. "Off-Policy Differentiable Logic Reinforcement Learning" ECML/PKDD (2) 2021: 617-632. (CCF B)
- Li Zhang, Xin Li, etc. "Universal Value Iteration Networks: When Spatially-invariant is Not Universal" AAAI 2020: 6778-6785. (CCF A)
- Xinwen Wang, Xin Li, etc. "On Improving the Learning of Long-Term historical Information for Tasks with Partial Observability" IEEE International Conference on Data Science in Cyberspace: Big Data and Business Analytics, 2020.
- Pengfei Zhu, Xin Li, Pascal Poupart, "On Improving Deep Reinforcement Learning for POMDPs". CoRR abs/1704.07978(2017). (cited over 104 times)
- Xin Li, William K. Cheung, Jiming Liu, "Improving POMDP’s Tractability Via Belief Compression and Clustering", IEEE Transaction on Systems, Man and Cybernetics – Part B 40(1):125-136 Feb, 2010. (CCF A)
- Xin Li, William K. Cheung, Jiming Liu, Zhili Wu, "A Novel Orthogonal NMF-Based Belief Compression for POMDPs", in Proceedings of 24th International Conference on Machine Learning (ICML), Pages: 537 -544 Corvallis, OR, US, 2007. (CCF A)
Representation Learning and More
P.S. we have another group led by A/Prof. Xin Li working on data mining, (deep) representation learning with applications to healthcare, social network analysis and recommender systems. We are also recruiting students to work on the related fields every year. For more details, please go to Xin's homepage@BIT. Here's our selected publications.
- Peiyao Zhao, Yuangang Pan, Xin Li, Xu Chen, Ivor W. Tsang, Lejian Liao. "Coarse-to-Fine Contrastive Learning on Graphs" TNNLS 2023.
- Qianyu Chen, Xin Li, Kunnan Geng, Mingzhong Wang. "Context-aware Safe Medication Recommendations with Molecular Graph and DDI Graph Embedding" AAAI 2023.
- Fuhao Yang, Xin Li, Min Wang, Hongyu Zang, Wei Pang, Mingzhong Wang. "WaveForM: Graph Enhanced Wavelet Learning for Long Sequence Forecasting of Multivariate Time Series" AAAI 2023.
- Huiting Hong, Xin Li, Yuangang Pan, Ivor W. Tsang. "Domain-Adversarial Network Alignment" IEEE TKDE 2022.
- Hongyu Zang, Dongcheng Han, Xin Li, Zhifeng Wan, Mingzhong Wang. "CHA: Categorical Hierarchy-based Attention for Next POI Recommendation" ACM TOIS 2022.
- Huiting Hong, Xin Li, Mingzhong Wang. "GANE: A Generative Adversarial Network Embedding" IEEE TNNLS 2020.
- Li Liu, Xin Li, William K. Cheung, Lejian Liao. "Structural Representation Learning for User Alignment Across Social Networks" IEEE TKDE 2020.
- Xin Li, Dongcheng Han, Jing He, Lejian Liao, Mingzhong Wang. "Next and Next New POI Recommendation via Latent Behavior Pattern Inference". ACM TOIS 2019.
- Rui Ye, Xin Li, et al. "A Vectorized Relational Graph Convolutional Network for Multi-Relational Network Alignment", IJCAI 2019. (CCF A)
- Jing He, Xin Li, Lejian Liao, et al, "Inferring Continuous Latent Preference on Transition Intervals for Next Point-of-Interest Recommendation" ECML/PKDD 2018. (CCF B)
- Shengnan Li, Xin Li, et al. "Non-translational Alignment for Multi-relational Networks" IJCAI 2018. (CCF A)
- Lin Liu, Xin Li, William K. Cheung, Chengcheng Xu, "A Structural Representation Learning for Multi-relational Networks" IJCAI 2017. (CCF A)
- Jing He, Xin Li, Lejian Liao, "Category-aware Next Point-of-Interest Recommendation via Listwise Bayesian Personalized Ranking" IJCAI 2017. (CCF A)
- Jing He, Xin Li, Lejian Liao, Dandan Song, William K. Cheung. "Inferring A Personalized Next Point-of-Interest Recommendation Model with Latent Behavior Patterns" AAAI 2016. (CCF A)
- Li Liu, William K. Cheung, Xin Li, Lejian Liao. "Aligning Users Across Social Networks Using Network Embedding" IJCAI 2016. (CCF A)