About Me

I am Haotian Wang from the National University of Defense Technology. I received my B.S., M.S., and Ph.D. degrees in Computer Science and Technology from the National University of Defense Technology. During my doctoral studies, I visited Tsinghua University and worked with Prof. Peng Cui on foundational theories and methods for causal inference. Besides, I also collaborate with Prof. Zhouchen Lin’s team at Peking University, Prof. Fei Wu’s team at Zhejiang University, and Prof. Xinwang Liu’s team at the National University of Defense Technology. My research interests include causal inference and strategic learning, with recent work spanning treatment effect estimation, counterfactual prediction, algorithmic recourse, strategic classification, and robust machine learning.

My Google Scholar profile is available here. The profile currently lists 59 publications and Google Scholar citations 765 .

Scholar Metrics

• Citations: 765 total, 745 since 2021
• h-index: 13 total, 13 since 2021
• i10-index: 20 total, 20 since 2021
• Research interests: Causal Inference; Strategic Agents

Publications

Selected Publications

Causal Inference

• 扩散模型引导的根因分析
  王浩天，周学广，王尚文，靳若春，黄万荣，杨文婧，王戟. 软件学报, CCF 中文A类期刊, 2025. | Citations: 0

• Unveiling Prior-data Fitted Networks on Causal Effect Estimation: Pre-training or Finetuning?
  H Wang, X Lv, H Zou, Y Xiao, S Gu, Y Shi, Y Mao, Y Zhang, M Geng, S Yang, H Li, W Yang, P Cui, Z Lin. International Conference on Machine Learning, ICML, CCF-A, 2026. | Citations: 0

• Transformers with Endogenous In-Context Learning: Bias Characterization and Mitigation
  H Wang, H Li, H Zou, H Chi, Y Shi, Y Zhang, W Yang, X Liu, Z Lin. The Fourteenth International Conference on Learning Representations, ICLR, CCF-A, 2026. | Citations: 0

• Effective and efficient time-varying counterfactual prediction with state-space models
  H Wang, H Li, H Zou, H Chi, L Lan, W Huang, W Yang. The Thirteenth International Conference on Learning Representations, ICLR, CCF-A, 2025. | Citations: 13

• Out-of-distribution generalization with causal feature separation
  H Wang, K Kuang, L Lan, Z Wang, W Huang, F Wu, W Yang. IEEE Transactions on Knowledge and Data Engineering, TKDE, CCF-A, 2023. | Citations: 39

• Estimating individualized causal effect with confounded instruments
  H Wang, W Yang, L Yang, A Wu, L Xu, J Ren, F Wu, K Kuang. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD, CCF-A, 2022. | Citations: 35

• Learning Feasible Causal Algorithmic Recourse without Prior Structure
  H Wang, H Zou, X Zhou, S Wang, L Lan, W Yang, P Cui. IEEE International Conference on Data Mining Workshops (ICDMW) CRL Workshop Best Paper, 805-813, 2024. | Citations: 1

• Treatment effect estimation with adjustment feature selection
  H Wang, K Kuang, H Chi, L Yang, M Geng, W Huang, W Yang. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD, CCF-A, 2023. | Citations: 27

• Learning Feasible Causal Algorithmic Recourse: A Prior Structural Knowledge Free Approach
  H Wang, H Zou, X Zhou, S Wang, W Yang, P Cui. Proceedings of the ACM on Web Conference, WWW, CCF-A, 2025. | Citations: 2

• Detecting Unobserved Confounders: A Kernelized Regression Approach
  Y Chen, Y Mao, C Zheng, H Zou, S Gu, S Liu, Y Shi, W Yang, K Kuang, H Wang. AAAI Conference on Artificial Intelligence, AAAI, CCF-A, 2026, corresponding author. | Citations: 0

• Pairwise similarity regularization for adversarial domain adaptation
  H Wang, W Yang, J Wang, R Wang, L Lan, M Geng. Proceedings of the 28th ACM International Conference on Multimedia, ACM MM, CCF-A, 2020. | Citations: 13

• Factual observation based heterogeneity learning for counterfactual prediction
  H Zou, H Wang, R Xu, B Li, J Pei, YJ Jian, P Cui. Conference on Causal Learning and Reasoning, CleaR, Causal Top Conference, 2023. | Citations: 12

• Your neighbor matters: Towards fair decisions under networked interference
  W Yang, H Wang, H Li, H Zou, R Jin, K Kuang, P Cui. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD, CCF-A, 2024, co-first and corresponding author. | Citations: 9

• Learning Kernelized Hypothesis for Hidden Confounder Detection
  Y Chen, H Wang, Y Mao, X Lv, S Yang, K Kuang, X Liu, W Yang. International Joint Conference on Artificial Intelligence, IJCAI, CCF-B, 2026, corresponding author. | Citations: 0

• Stable prediction with leveraging seed variable
  K Kuang, H Wang, Y Liu, R Xiong, R Wu, W Lu, Y Zhuang, F Wu, P Cui, B Li. IEEE Transactions on Knowledge and Data Engineering, TKDE, CCF-A, 2022. | Citations: 16

• PDMC: Generating Feasible Algorithmic Recourse via Perturbation Data Manifold Constraint
  Z Wang, H Zou, H Yu, S Fan, H Wang, Y He, P Cui. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD, CCF-A, 2025. | Citations: 1

• Automated Exposure Mapping for Networked Interference
  Y Mao, H Wang, Y Cai, M Li, J Wang, W Yang. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, CCF-B, 2025, corresponding author. | Citations: 1

• Modality re-balance for visual question answering: A causal framework
  X Lv, W Huang, H Wang, R Jin, X Li, Z Lin, S Li, Y Feng, Y Tang. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, CCF-B, 2024. | Citations: 2

Strategic Agents

• Competing for shareable arms in multi-player multi-armed bandits
  R Xu, H Wang, X Zhang, B Li, P Cui. International Conference on Machine Learning, ICML, 2023, co-first author. | Citations: 12

• Beyond Rational Illusion: Behaviorally Realistic Strategic Classification
  X Lv, Y Mao, R Xu, C Zheng, J Yang, Y Chen, Y Chen, Y Shi, Y Zhang, H Li, W Yang, S Yang, Z Lin, H Wang. International Conference on Machine Learning, ICML, CCF-A, 2026, corresponding author. | Citations: 0

• When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach
  X Lv, Y Mao, R Xu, C Zheng, H Li, Y Chen, J Yang, W Huang, Y Chen, M Geng, S Liu, K Kuang, S Yang, W Yang, Z Lin, H Wang. International Conference on Machine Learning, ICML, CCF-A, 2026, corresponding author. | Citations: 0

• Domain specified optimization for deployment authorization
  H Wang, H Chi, W Yang, Z Lin, M Geng, L Lan, J Zhang, D Tao. Proceedings of the IEEE/CVF International Conference on Computer Vision, ICCV,CCF-A, 2023. | Citations: 10

• Failure Localization in Multi-Agent Code Generation via Knowledge-Guided and Transferable Reasoning
  M Geng, S Gu, Z Liu, C Xu, Z Qu, H Wang. AAAI Conference on Artificial Intelligence, AAAI, CCF-A, 2026, corresponding author. | Citations: 0

• Beyond Independent Manipulation: Individual Fairness-aware Strategic Classification with Peer Imitation
  X Lv, Y Mao, R Xu, J Yang, C Zheng, Y Chen, W Huang, S Yang, W Yang, P Cui, X Liu, H Wang. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD, CCF-A, 2026, corresponding author. | Citations: 0

• Advanced strategic improvement with decision interactions
  W Yang, X Lv, Y Mao, L Xu, R Jin, H Chen, J Ren, J Yang, Y Chen, H Wang. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, PKDD-ECML, CCF-B, 2025, corresponding author. | Citations: 2

• Partial Fairness Awareness: Belief-Guided Strategic Mechanism for Strategic Agents
  X Lv, C Zheng, Y Mao, R Xu, H Zou, S Gu, L Xu, H Chen, Y Chen, W Yang, H Wang. AAAI Conference on Artificial Intelligence, AAAI, CCF-A, 2026, corresponding author. | Citations: 0

• Breaking the Gradient Barrier: Unveiling Large Language Models for Strategic Classification
  X Lv, Y Mao, H Li, K Liang, J Yang, W Huang, H Chi, H Chen, L Lan, Y Chen, W Yang, H Wang. The Thirty-ninth Annual Conference on Neural Information Processing Systems, NeurIPS, CCF-A, 2025, corresponding author. | Citations: 1

• Mitigating Collaboration Degeneration in Multi-Agent Code Generation via a Controllable Competitive Collaboration Approach
  S Gu, M Geng, Y Dong, Y Mao, Z Qu, H Zou, R Jin, Z Liu, C Xu, H Wang. International Joint Conference on Artificial Intelligence, IJCAI, CCF-B, 2026, corresponding author. | Citations: 0

• Ppa-game: Characterizing and learning competitive dynamics among online content creators
  R Xu, H Wang, X Zhang, B Li, P Cui. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD, CCF-A, 2025. | Citations: 5

Multi-modal LLMs

• Beyond Fixed Biases: Decoding the Role of Reasoning Uncertainty in MLLM Modality Conflicts
  Z Zhang, T Wang, X Gong, Y Shi, H Wang, D Wang, L Hu. International Conference on Machine Learning, ICML, CCF-A, 2026, corresponding author. | Citations: 0

• Mme-videoocr: Evaluating ocr-based capabilities of multimodal llms in video scenarios
  Y Shi, H Wang, W Xie, H Zhang, L Zhao, YF Zhang, X Li, C Fu, Z Wen, et. al. The Thirty-ninth Annual Conference on Neural Information Processing Systems, NeurIPS, CCF-A, 2025, corresponding author. | Citations: 17

• Realunify: Do unified models truly benefit from unification? a comprehensive benchmark
  Y Shi, Y Dong, Y Ding, Y Wang, X Zhu, S Zhou, W Liu, H Tian, R Wang, et.al. The Thirty-ninth Annual Conference on Neural Information Processing Systems, NeurIPS, CCF-A, 2025, corresponding author. | Citations: 16

• Large language models are few-shot summarizers: Multi-intent comment generation via in-context learning
  M Geng, S Wang, D Dong, H Wang, G Li, Z Jin, X Mao, X Liao. Proceedings of the 46th IEEE/ACM International Conference on Software Engineering, ICSE, CCF-A, 2024. | Citations: 258

• GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K Resolution
  F Wang, M Chen, Y Li, D Wang, H Wang, Z Guo, Z Wang, B Shan, L Lan, Y Wang, H Wang, W Yang, Bo D, J Zhang. The Thirty-ninth Annual Conference on Neural Information Processing Systems, NeurIPS, CCF-A, 2025. | Citations: 21

• Opengpt-4o-image: A comprehensive dataset for advanced image generation and editing
  Z Chen, X Bai, Y Shi, C Fu, H Zhang, H Wang, X Sun, Z Zhang, L Wang, Y Zhang, P Wang, Y Zhang. International Conference on Machine Learning, ICML, CCF-A, 2026. | Citations: 18

• Basereward: A strong baseline for multimodal reward model
  YF Zhang, H Yang, H Zhang, Y Shi, Z Chen, H Tian, C Fu, H Wang, K Wu, B Cui, X Wang, J Pan, Z Zhang, L Wang. The Fourteenth International Conference on Learning Representations, ICLR, CCF-A, 2026. | Citations: 8

• Dynamic data selection with normalized gradient-based influence approximation for targeted fine-tuning of llms
  Z Wang, Q Zhu, F Mi, Y Wang, H Wang, L Shang. Knowledge-Based Systems, 2025. | Citations: 3

• Mitigating Sensitive Information Leakage in LLMs4Code through Machine Unlearning
  S Gu, Z Qu, R Geng, M Geng, S Wang, C Xu, H Wang, Z Lin, D Dong. Neural Networks, CCF-B, 2026. | Citations: 4

• Context-Driven Index Trimming: A Data Quality Perspective to Enhancing Precision of RALMs
  K Ma, R Jin, W Haotian, W Xi, H Chen, Y Tang, Q Wang. Conference on Empirical Methods in Natural Language Processing, EMNLP, CCF-B, 2024. | Citations: 2

Other Publications

• Cauchy sparse NMF with manifold regularization: A robust method for hyperspectral unmixing
  H Wang, W Yang, N Guan. Knowledge-Based Systems, 2019. | Citations: 44

• Anchor-Prompt-based Segmentation and Embedding Model
  S Li, Z Lin, H Wang, W Yang, H Liu. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, CCF-B, 2025, corresponding author. | Citations: 0

• Make Model Transparent: Brain Network Analysis via Causal and Knowledge Graph Learning
  L Meng, K Liang, H Yu, H Wang, M Li, X Liu. AAAI Conference on Artificial Intelligence, AAAI, CCF-A, 2026. | Citations: 0

• Invariant Learning on Heterogeneous Graphs via Subgraph Environment Inference
  Y Fu, Y Wang, H Zou, Y He, H Wang, Q Cheng, G Cheng, S Liu. Proceedings of the ACM Web Conference , WWW, 968-979, 2026. | Citations: 0

• FD-TE Diagnosis: Enhancing Microservice Fault Diagnosis With Frequency Domain Features and Centrality-Aware Time Encoding
  M Liu, Y Li, H Wang, M Li, F Tang, W Yang. IEEE Transactions on Reliability, 2026. | Citations: 0

• Width-Enhanced Fine-Tuning for Long-Tailed Learning
  Y Liu, H Wang, Y Zhang, X Li, S Yang. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, CCF-B, 2026. | Citations: 0

• Causal Effect Estimation under Network Interference with State Space Models
  Y Mao, Y Cai, Z Li, H Wang, M Li, W Yang, J Wang. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, CCF-B, 2026. | Citations: 0

• Rademacher dropout: An adaptive dropout for deep neural network via optimizing generalization gap
  H Wang, W Yang, Z Zhao, T Luo, J Wang, Y Tang. Neurocomputing 357, 177-187, 2019. | Citations: 34

• TMDA: Task-specific multi-source domain adaptation via clustering embedded adversarial training
  H Wang, W Yang, Z Lin, Y Yu. IEEE International Conference on Data Mining, ICDM, CCF-B, 2019. | Citations: 32

• Unbiased recommender learning from implicit feedback via weakly supervised learning
  H Wang, Z Chen, H Wang, Y Tan, L Pan, T Liu, X Chen, H Li, Z Lin. Forty-second International Conference on Machine Learning, ICML, CCF-A, 2025. | Citations: 13

• Scaling few-shot learning for the open world
  Z Lin, W Yang, H Wang, H Chi, L Lan, J Wang. AAAI Conference on Artificial Intelligence, AAAI, CCF-A, 2024. | Citations: 7

• Text-guided multi-class multi-object tracking for fine-grained maritime rescue
  S Li, Z Lin, H Wang, W Yang, H Liu. Remote Sensing, CCF-B, 2024. | Citations: 4

• Environment Inference for Learning Generalizable Dynamical System
  S Liu, Y He, H Wang, W Yang, Y Wang, P Cui, Z Liu. The Thirty-ninth Annual Conference on Neural Information Processing Systems, NeurIPS, CCF-A, 2025. | Citations: 6

• An effective few-shot learning approach via location-dependent partial differential equation
  H Wang, Z Zhao, Y Tang. Knowledge and Information Systems, 2020. | Citations: 6

Enterprise

• [2025-12] 王浩天入选CCF-2025博士学位论文激励计划！
• [2025-11] 王浩天入选全军优秀博士学位论文！
• [2022] 王浩天入选全军优秀硕士学位论文！