Haohan Wang's Publication

---2024---

Leng J, Li Y, Wang H. Choosing Wisely and Learning Deeply: Selective Cross-Modality Distillation via CLIP for Domain Generalization , in proceedings of 2024 Transactions of Machine Learning Research TMLR 2024

Zhang P, Liu H, Li C, Xie X, Kim S, Wang H Foundation model-oriented robustness: Robust image model evaluation with pretrained models , in proceedings of 2023 International Conference on Learning Representations. ICLR 2024

Miranda O, Fan P, Qi X, Wang H, Brannock MD, Kosten TR, Ryan ND, Kirisci L, Wang L. DeepBiomarker2: Prediction of alcohol and substance use disorder risk in post-traumatic stress disorder patients using electronic medical records and multiple social determinants of health. , Journal of Personalized Medicine. 2024 Jan 14;14(1):94.

Miranda O, Fan P, Qi X, Wang H, Brannock MD, Kosten T, Ryan ND, Kirisci L, Wang L. Prediction of adverse events risk in patients with comorbid post-traumatic stress disorder and alcohol use disorder using electronic medical records by deep learning models , Drug and alcohol dependence. 2024 Feb 1;255:111066.

Zhang J, Song B, Wang H, Han B, Liu T, Liu L, Sugiyama M. BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning , IEEE Transactions on Pattern Analysis and Machine Intelligence (2024). IEEE PAMI 2024

---2023---

Lee KY, Wang H, Yook Y, Rhodes JS, Christian-Hinman CA, Tsai NP Tumor suppressor p53 modulates activity-dependent synapse strengthening, autism-like behavior and hippocampus-dependent learning. , Molecular Psychiatry. 2023 Sep;28(9):3782-94

Zhou A, Wang J, Wang YX, Wang H. Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models. , in Neural Information Processing Systems. 2024 Feb 13;36. NeurIPS 2023

Bao W, Wei T, Wang H, He J. Adaptive Test-Time Personalization for Federated Learning , in Neural Information Processing Systems. 2024 Feb 13;36. NeurIPS 2023

Huang Z, Zhou A, Ling Z, Cai M, Wang H, Lee YJ. A Sentence Speaks a Thousand Images: Domain Generalization through Distilling CLIP with Language Guidance , InProceedings of the IEEE/CVF International Conference on Computer Vision 2023 (pp. 11685-11695). ICCV 2023

Bao, W., Wang, H., Wu, J., & He, J. Optimizing the Collaboration Structure in Cross-Silo Federated Learning , in proceedings of 2023 International Conference on Machine Learning. ICML 2023

Zhang, P., Guo, J., Li, C., Xie, Y., Kim, J. B., Zhang, Y., Xie, X., Wamg, H., & Kim, S. Efficiently leveraging multi-level user intent for session-based recommendation via atten-mixer network , In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, WSDM 2023, pp. 168-176. (Best Paper Honorable Mention)

Xiang, K., Zhang, X., She, J., Liu, J., Wang, H., Deng, S., & Jiang, S. Toward Robust Diagnosis: A Contour Attention Preserving Adversarial Defense for COVID-19 Detection , In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 3, pp. 2928-2937. AAAI 2023

Zhang Y.* and Wang H. Photong: Generating 16-Bar Melodies from Images, AAAI 2023 Workshop creativeAI homepage 2023 (* K-12 Author)

---2022---

Wang H.,Toward Robust Machine Learning by Countering Superficial Features, Phd Thesis 2022

Wang H., Lopez O, Xing E. P., and Wu W.Kernel Mixed Model for Transcriptome Association Study, Journal of Computational Biology 2022

Wang, H., Huang Z., Wu X., Xing E.P. Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD 2022

Wang, H., Huang Z., Zhang H., Lee Y.J., Xing E.P. Toward Learning Human-aligned Cross-domain Robust Models by Countering Misaligned Features, Proceedings of the Uncertainty in Artificial Intelligence UAI 2022

Wang X, Wang, H., Yang D. Measure and Improve Robustness in NLP Models: A Survey, Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics NAACL 2022

Wang, H.*, Huang, Z.*, Huang, D., Lee, Y. J., & Xing, E. P.The Two Dimensions of Worst-Case Training and Their Integrated Effect for Out-of-Domain Generalization, Proceedings of the 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics CVPR 2022

Miranda, O., Fan P., Qi X., Yu Z., Ying J., Wang H., Brent D.A., Silverstein J.C., Chen Y., Wang L.DeepBiomarker: identifying important lab tests from electronic medical records for the prediction of suicide-related events among PTSD patients, Journal of personalized medicine, 12(4), 524.

Wang H., Lopez O, Wu W., and Xing E. P.,A Network-structured High-dimension Variable Selection Method with P-values, for Gene Set Prioritization with Transcriptome Association Study Guided by Regulatory Network, International Conference on Research in Computational Molecular Biology RECOMB 2022

---2021---

Wang H., Aragam B., and Xing E. P.,Tradeoffs of Linear Mixed Models in Genome-wide Association Studies, Journal of Computational Biology 29.3 (2022): 233-242

Songwei Ge, Mishra S., Wang H, Li, C., and Jacobs. D., Robust Contrastive Learning Using Negative Samples with Diminished Semantics, Advances in Neural Information Processing Systems (NeurIPS 2021).

Wang H., Pei F., Vanyukov MM., Bahar I., Wu W., Xing E. P.,Coupled Mixed Model for Joint Genetic Analysis of Complex Disorders with Two Independently Collected Data Sets, BMC bioinformatics 22.1 (2021): 1-14

Du X., Wang H., Zhu Z., Zeng X., Chang YW., Zhang J., and Xu M.Active Learning to Classify Macromolecular Structures in Situ for Less Supervision in Cryo-electron Tomography, Bioinformatics (2021)

---2020---

Zheng Y., Wang H., Yang Z., Gao X., Xing E. P.,and Xu M, Poly(A)-DG: a Deep-learning-based Domain Generalization Method to Identify Cross-species Poly(A) Signal without Prior Knowledge from Target Species, PLOS Computational Biology, 16(11): e1008297

Wang H, Huang Z., Xing E. P.,and Huang D, and Self-Challenging Improves Cross-Domain Generalization, Proceedings of European Conference on Computer Vision, (ECCV 2020) (ECCV 2020). (top 2% Oral)

Wang H, Wu X., Huang Z., and Xing E. P., High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020). (top 2% Oral)

Ge, S., Wang, H.., Alavi, A., Xing, E. and Bar-Joseph, Z, Supervised Adversarial Alignment of scRNA-seq Data, Proceedings of 24th International Conference on Research in Computational Molecular Biology (RECOMB 2020).

Wang, H., Vanyukov, MM., Xing, EP. & Wu, W , Discovering Weaker Genetic Associations Guided by Known Associations, BMC medical genomics, 13(3), 1-10.

Wang, H., Yue, T., Yang, J., Wu, W., & Xing, EP., Deep Mixed Model for Marginal Epistasis Detection and Population Stratification Correction in Genome-wide Association Studies, BMC bioinformatics, 20(23), 1-11..

---2019---

Wang H, Ge S., Lipton Z. C. , and Xing E. P., Learning Robust Global Representations by Penalizing Local Predictive Power, Proceedings of Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2019).

Wang H, He Z., Lipton Z. C. , and Xing E. P., Learning Robust Representations by Projecting Superficial Statistics Out, Proceedings of Seventh International Conference on Learning Representations (ICLR 2019). (top 1% Oral)

Wang H, Sun D., and Xing, E. P. What If We Simply Swap the Two Text Fragments? A Straightforward yet Effective Way to Test the Robustness of Methods to Confounding Signals in Nature Language Inference Tasks, Proceedings of Thirty-third AAAI Conference on Artificial Intelligence (AAAI 2019). (Oral)

Wang H, Wu Z., and Xing E. P., Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications, Proceedings of 24th Pacific Symposium on Biocomputing (PSB 2019). (Oral)

Wang H, Liu X., Tao Y., Ye W., Jin Q., Cohen W. W., and Xing E. P., Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning, Proceedings of 24th Pacific Symposium on Biocomputing (PSB 2019).

Xiao Y, Chen D, Wei S, Li Q, Wang H, and Xu M. Rumor Propagation Dynamic Model Based on Evolutionary Game and Anti-rumor, Nonlinear Dynamics. 2019 Jan 1;95(1):523-39.

---2018---

Wang H, Lengerich B. J., Aragam B., and Xing E. P., Precision Lasso: Accounting for Correlations and Linear Dependencies in High-Dimensional Genomic Data, Bioinformatics, PMID: 30184048 DOI:10.1093/bioinformatics/bty750 , 2018.

Wang H, Liu X., Xiao Y., Xu M. and Xing E. P., Multiplex Confounding Factor Correction for Genomic Association Mapping with Squared Sparse Linear Mixed Model, Methods, 2018 Aug 1; 145: 33?40.

Wang H, Aragam B. and Xing E. P., Variable Selection in Heterogeneous Datasets: A Truncated-rank Sparse Linear Mixed Model with Applications to Genome-wide Association Studies, Methods, 2018 2018 Aug 1; 145: 2?9.

Yue, T., & Wang H, Deep Learning for Genomics: A Concise Overview, arXiv preprint arXiv:1802.00810.

---2017---

Lee, S., Wang, H., & Xing, EP., Backward Genome-Transcriptome-phenome Association Mapping, Methods, 129, 18-23.

Wang, H. & Raj, B., On the Origin of Deep Learning, arXiv preprint arXiv:1702.07800.

Wang, H., Meghawat, A., Morency, L. P., & Xing, E. P., Select-Additive Learning: Improving Generalization Multimodal Sentiment Analysis, In 2018 IEEE International Conference on Multimedia and Expo (ICME 2017). [Data][Code]

---2016---

Yang, J., Wang, H., Zhu, J., & Xing, E. P., SeDMiD for Confusion Detection: Mind State from Time Series Brain Wave Data, In TSW workshop NIPS 2016

Wang H, Yang, J., Multiple Confounders Correction with Regularized Linear Mixed Effect Models, with Application in Biological Processes.,in 2016 IEEE International Conference on Bioinformatics and Biomedicine. (BIBM 16)

---Previous---

Gupta, A., Wang, H., & Ganapathiraju, M., Learning Structure in Gene Expression Data Using Deep Architectures, an Application to Gene Clustering.,in 2015 IEEE International Conference on Bioinformatics and Biomedicine. (BIBM 15)

Moon, S., Kim, S., & Wang, H., Multimodal Transfer Deep Learning for Audio Visual Recognition, MMML Workshop NIPS 2015

Wang, H., Raza, A. A., Lin, Y., & Rosenfeld, R., Behavior Analysis of Low-literate Users of a Viral Speech-based Telephone Service.,In Proceedings of the 4th Annual Symposium on Computing for Development. (ACM DEV 13)

Wang, H., Li, Y., Hu, X., Yang, Y., Meng, Z., & Chang, K. M., Using EEG to Improve Massive Open Online Courses Feedback Interaction, In AIED Workshops. [Data][Code][Video]

Publications

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