Research outputs
2025
Self Data Augmentation for Open Domain Question Answering
Zhang, Q., Zheng, M., Chen, S., Liu, H., & Fang, M. (2025). Self Data Augmentation for Open Domain Question Answering. ACM Transactions on Information Systems, 43(2), 1-35. doi:10.1145/3707449
HASARD: A BENCHMARK FOR VISION-BASED SAFE REINFORCEMENT LEARNING IN EMBODIED AGENTS
Tomilin, T., Fang, M., & Pechenizkiy, M. (2025). HASARD: A BENCHMARK FOR VISION-BASED SAFE REINFORCEMENT LEARNING IN EMBODIED AGENTS. In 13th International Conference on Learning Representations Iclr 2025 (pp. 9304-9336).
Integrating Large Language Models with Reinforcement Learning for Generalization in Strategic Card Games: Extended Abstract
Xia, W., Fang, M., Guo, Z., Du, Y., & Xu, B. (2025). Integrating Large Language Models with Reinforcement Learning for Generalization in Strategic Card Games: Extended Abstract. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems Aamas (pp. 2795-2797).
MONTE CARLO PLANNING WITH LARGE LANGUAGE MODEL FOR TEXT-BASED GAME AGENTS
Shi, Z., Fang, M., & Chen, L. (2025). MONTE CARLO PLANNING WITH LARGE LANGUAGE MODEL FOR TEXT-BASED GAME AGENTS. In 13th International Conference on Learning Representations Iclr 2025 (pp. 72995-73015).
RuAG: LEARNED-RULE-AUGMENTED GENERATION FOR LARGE LANGUAGE MODELS
Zhang, Y., Xiao, P., Wang, L., Zhang, C., Fang, M., Du, Y., . . . Zhang, Q. (2025). RuAG: LEARNED-RULE-AUGMENTED GENERATION FOR LARGE LANGUAGE MODELS. In 13th International Conference on Learning Representations Iclr 2025 (pp. 23316-23339).
TACKLING DATA CORRUPTION IN OFFLINE REINFORCEMENT LEARNING VIA SEQUENCE MODELING
Xu, J., Yang, R., Qiu, S., Luo, F., Fang, M., Wang, B., & Han, L. (2025). TACKLING DATA CORRUPTION IN OFFLINE REINFORCEMENT LEARNING VIA SEQUENCE MODELING. In 13th International Conference on Learning Representations Iclr 2025 (pp. 67875-67903).
TOWARDS EMPOWERMENT GAIN THROUGH CAUSAL STRUCTURE LEARNING IN MODEL-BASED REINFORCEMENT LEARNING
Cao, H., Feng, F., Fang, M., Dong, S., Yang, T., Huo, J., & Gao, Y. (2025). TOWARDS EMPOWERMENT GAIN THROUGH CAUSAL STRUCTURE LEARNING IN MODEL-BASED REINFORCEMENT LEARNING. In 13th International Conference on Learning Representations Iclr 2025 (pp. 88829-88863).
2024
Augmenting biomedical named entity recognition with general-domain resources.
Yin, Y., Kim, H., Xiao, X., Wei, C. H., Kang, J., Lu, Z., . . . Chen, Q. (2024). Augmenting biomedical named entity recognition with general-domain resources.. Journal of biomedical informatics, 159, 104731. doi:10.1016/j.jbi.2024.104731
Unsupervised Multiple Choices Question Answering Via Universal Corpus
Zhang, Q., Ge, H., Chen, X., & Fang, M. (2024). Unsupervised Multiple Choices Question Answering Via Universal Corpus. In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 11771-11775). IEEE. doi:10.1109/icassp48485.2024.10446538
Human-Guided Moral Decision Making in Text-Based Games
Shi, Z., Fang, M., Chen, L., Du, Y., & Wang, J. (2024). Human-Guided Moral Decision Making in Text-Based Games. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 38 (pp. 21574-21582). Association for the Advancement of Artificial Intelligence (AAAI). doi:10.1609/aaai.v38i19.30155
Large Language Models Are Neurosymbolic Reasoners
Fang, M., Deng, S., Zhang, Y., Shi, Z., Chen, L., Pechenizkiy, M., & Wang, J. (2024). Large Language Models Are Neurosymbolic Reasoners. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 38 (pp. 17985-17993). Association for the Advancement of Artificial Intelligence (AAAI). doi:10.1609/aaai.v38i16.29754
Representation-Based Robustness in Goal-Conditioned Reinforcement Learning
Yin, X., Wu, S., Liu, J., Fang, M., Zhao, X., Huang, X., & Ruan, W. (2024). Representation-Based Robustness in Goal-Conditioned Reinforcement Learning. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 38 (pp. 21761-21769). Association for the Advancement of Artificial Intelligence (AAAI). doi:10.1609/aaai.v38i19.30176
Dynamic Truck–UAV Collaboration and Integrated Route Planning for Resilient Urban Emergency Response
Long, Y., Xu, G., Zhao, J., Xie, B., & Fang, M. (2023). Dynamic Truck–UAV Collaboration and Integrated Route Planning for Resilient Urban Emergency Response. IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT. doi:10.1109/TEM.2023.3299693
MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning
Grooten, B., Taylor, M. E., Tomilin, T., Mahmood, A. R., Vasan, G., Fang, M., . . . Mocanu, D. C. (2024). MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems Aamas Vol. 2024-May (pp. 733-742).
MedINST: Meta Dataset of Biomedical Instructions
Han, W., Fang, M., Zhang, Z., Yin, Y., Song, Z., Chen, L., . . . Chen, Q. (2024). MedINST: Meta Dataset of Biomedical Instructions. In Findings of the Association for Computational Linguistics: EMNLP 2024 (pp. 8221-8240). Association for Computational Linguistics. doi:10.18653/v1/2024.findings-emnlp.482
Policy Learning from Tutorial Books via Understanding, Rehearsing and Introspecting
Chen, X. H., Wang, Z., Du, Y., Jiang, S., Fang, M., Yu, Y., & Wang, J. (2024). Policy Learning from Tutorial Books via Understanding, Rehearsing and Introspecting. In Advances in Neural Information Processing Systems Vol. 37.
RetrievalQA: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answering
Zhang, Z., Fang, M., & Chen, L. (2024). RetrievalQA: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answering. In Findings of the Association for Computational Linguistics ACL 2024 (pp. 6963-6975). Association for Computational Linguistics. doi:10.18653/v1/2024.findings-acl.415
Revisiting Catastrophic Forgetting in Large Language Model Tuning
Li, H., Ding, L., Fang, M., & Tao, D. (2024). Revisiting Catastrophic Forgetting in Large Language Model Tuning. In Findings of the Association for Computational Linguistics: EMNLP 2024 (pp. 4297-4308). Association for Computational Linguistics. doi:10.18653/v1/2024.findings-emnlp.249
TASK ADAPTATION FROM SKILLS: INFORMATION GEOMETRY, DISENTANGLEMENT, AND NEW OBJECTIVES FOR UNSUPERVISED REINFORCEMENT LEARNING
Yang, Y., Zhou, T., He, Q., Han, L., Pechenizkiy, M., & Fang, M. (2024). TASK ADAPTATION FROM SKILLS: INFORMATION GEOMETRY, DISENTANGLEMENT, AND NEW OBJECTIVES FOR UNSUPERVISED REINFORCEMENT LEARNING. In 12th International Conference on Learning Representations Iclr 2024.
2023
Discourse-Aware Graph Networks for Textual Logical Reasoning
Huang, Y., Liu, L., Xu, K., Fang, M., Lin, L., & Liang, X. (2023). Discourse-Aware Graph Networks for Textual Logical Reasoning. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 45(10), 11668-11688. doi:10.1109/TPAMI.2023.3280178
Prescribed Safety Performance Imitation Learning From a Single Expert Dataset
Cheng, Z., Shen, L., Zhu, M., Guo, J., Fang, M., Liu, L., . . . Tao, D. (2023). Prescribed Safety Performance Imitation Learning From a Single Expert Dataset. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 45(10), 12236-12249. doi:10.1109/TPAMI.2023.3287908
Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost
Yin, L., Liu, S., Fang, M., Huang, T., Menkovski, V., & Pechenizkiy, M. (2023). Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 37 (pp. 10945-10953). Association for the Advancement of Artificial Intelligence (AAAI). doi:10.1609/aaai.v37i9.26297
Dual-Modality Co-Learning for Unveiling Deepfake in Spatio-Temporal Space
Guan, J., Zhou, H., Guo, Z., Hu, T., Deng, L., Quan, C., . . . Zhao, Y. (2023). Dual-Modality Co-Learning for Unveiling Deepfake in Spatio-Temporal Space. In PROCEEDINGS OF THE 2023 ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL, ICMR 2023 (pp. 85-94). doi:10.1145/3591106.3592284
Dynamic Contrastive Distillation for Image-Text Retrieval
Rao, J., Ding, L., Qi, S., Fang, M., Liu, Y., Shen, L., & Tao, D. (2023). Dynamic Contrastive Distillation for Image-Text Retrieval. IEEE Transactions on Multimedia, 1-13. doi:10.1109/tmm.2023.3236837
Shared dynamics learning for large-scale traveling salesman problem
Xu, Y., Fang, M., Chen, L., Du, Y., Xu, G., & Zhang, C. (2023). Shared dynamics learning for large-scale traveling salesman problem. ADVANCED ENGINEERING INFORMATICS, 56. doi:10.1016/j.aei.2023.102005
Joint reasoning with knowledge subgraphs for Multiple Choice Question Answering
Zhang, Q., Chen, S., Fang, M., & Chen, X. (2023). Joint reasoning with knowledge subgraphs for Multiple Choice Question Answering. INFORMATION PROCESSING & MANAGEMENT, 60(3). doi:10.1016/j.ipm.2023.103297
Deep Reinforcement Learning for Real-Time Assembly Planning in Robot-Based Prefabricated Construction
Zhu, A., Dai, T., Xu, G., Pauwels, P., de Vries, B., & Fang, M. (2023). Deep Reinforcement Learning for Real-Time Assembly Planning in Robot-Based Prefabricated Construction. IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING. doi:10.1109/TASE.2023.3236805
A Survey for Efficient Open Domain Question Answering
Zhang, Q., Chen, S., Xu, D., Cao, Q., Chen, X., Cohn, T., & Fang, M. (2023). A Survey for Efficient Open Domain Question Answering. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 14447-14465). Association for Computational Linguistics. doi:10.18653/v1/2023.acl-long.808
Are Large Kernels Better Teachers than Transformers for ConvNets?
Huang, T., Yin, L., Zhang, Z., Shen, L., Fang, M., Pechenizkiy, M., . . . Liu, S. (2023). Are Large Kernels Better Teachers than Transformers for ConvNets?. In Proceedings of Machine Learning Research Vol. 202 (pp. 14023-14038).
CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models
Zhao, J., Fang, M., Shi, Z., Li, Y., Chen, L., & Pechenizkiy, M. (2023). CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics. doi:10.18653/v1/2023.acl-long.757
Dynamic Sparsity Is Channel-Level Sparsity Learner
Yin, L., Li, G., Fang, M., Shen, L., Huang, T., Wang, Z., . . . Liu, S. (2023). Dynamic Sparsity Is Channel-Level Sparsity Learner. In Advances in Neural Information Processing Systems Vol. 36.
How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances
Zhang, Z., Fang, M., Chen, L., Namazi-Rad, M. -R., & Wang, J. (2023). How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. doi:10.18653/v1/2023.emnlp-main.516
Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach
Zhang, Y., Du, Y., Huang, B., Wang, Z., Wang, J., Fang, M., & Pechenizkiy, M. (2023). Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach. In Advances in Neural Information Processing Systems Vol. 36.
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist
Nimah, I., Fang, M., Menkovski, V., & Pechenizkiy, M. (2023). NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1240-1266). Association for Computational Linguistics. doi:10.18653/v1/2023.acl-long.69
REST: Enhancing Group Robustness in DNNs Through Reweighted Sparse Training
Zhao, J., Yin, L., Liu, S., Fang, M., & Pechenizkiy, M. (2023). REST: Enhancing Group Robustness in DNNs Through Reweighted Sparse Training. In Unknown Conference (pp. 313-329). Springer Nature Switzerland. doi:10.1007/978-3-031-43415-0_19
STAY MORAL AND EXPLORE: LEARN TO BEHAVE MORALLY IN TEXT-BASED GAMES
Shi, Z., Fang, M., Xu, Y., Chen, L., & Du, Y. (2023). STAY MORAL AND EXPLORE: LEARN TO BEHAVE MORALLY IN TEXT-BASED GAMES. In 11th International Conference on Learning Representations Iclr 2023.
Self-imitation Learning for Action Generation in Text-based Games
Shi, Z., Xu, Y., Fang, M., & Chen, L. (2023). Self-imitation Learning for Action Generation in Text-based Games. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (pp. 703-726). Association for Computational Linguistics. doi:10.18653/v1/2023.eacl-main.50
2022
Learning Granularity-Unified Representations for Text-to-Image Person Re-identification
Shao, Z., Zhang, X., Fang, M., Lin, Z., Wang, J., & Ding, C. (2022). Learning Granularity-Unified Representations for Text-to-Image Person Re-identification. In Proceedings of the 30th ACM International Conference on Multimedia (pp. 5566-5574). ACM. doi:10.1145/3503161.3548028
RETHINKING GOAL-CONDITIONED SUPERVISED LEARNING AND ITS CONNECTION TO OFFLINE RL
Yang, R., Lu, Y., Li, W., Sun, H., Fang, M., Du, Y., . . . Zhang, C. (2022). RETHINKING GOAL-CONDITIONED SUPERVISED LEARNING AND ITS CONNECTION TO OFFLINE RL. In Iclr 2022 10th International Conference on Learning Representations.
TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack
Cao, Y., Li, D., Fang, M., Zhou, T., Gao, J., Zhan, Y., & Tao, D. (2022). TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. doi:10.18653/v1/2022.emnlp-main.821
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets
Huang, T., Chen, T., Fang, M., Menkovski, V., Zhao, J., Yin, L., . . . Liu, S. (2022). You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets. In Proceedings of Machine Learning Research Vol. 198.