Publications

Thesis:

Yang, H., Nonparametric Bayesian Model for High Dimensional and Sparse Data, Duke University, 2010.

Journals:

1. Yang, H., Dunson, D.B. and O'Brien, S., Nonparametric Bayes stochastically ordered latent class models, Journal of the American Statistical Association. 106(495): 807-817 (2011)

2. Yang, H., Banks, D.L., Vivar, J.C and Dunson, D.B., The Multiple Bayesian Elastic Net, JSM Proceedings, Section on Bayesian Statistical Science. Miami, Florida: American Statistical Association (2011)

3. Yang, H., Wang, J. and Mojsilovic, A., Cascade Model with Dirichlet Process for Analyzing Multiple Dyadic Matrices, Journal of Applied Statistics. 39(9): 1991-2003 (2012).

4. Das, S., Yang, H. and Banks, D.L., Synthetic Priors from Analysis of Multiple Expert's Opinion, Statistics, Politics, and Policy. 3(3): 2151-7509 (2012).

5. Yang, H., Li, F., Schistermanc, E.F., Mumford, S.L. and Dunson, D.B., Bayesian inference on dependence in multivariate longitudinal data. 2012.

6. Yang, H., Liu, F., Ji, C. and Dunson, D.B., Adaptive Sampling for Bayesian Geospatial models , Statistics and Computing. Sep 2013, 1573-1375.

7. Yang, H. and Wang, J., Bayesian Hierarchical Kernelized Probabilistic Matrix Factorization, Communications in Statistics, 2014.

8. Yang, H. and Lazono, A., Multi-relational Learning via Hierarchical Nonparametric Bayesian Collective Matrix Factorization, Journal of Applied Statistics, 2015.

9. Yang, H. and Amemiya, Y., An Approach to Enterprise Revenue Forecasting as a Decision Support System, IBM Journal of Research and Development: Special Issue in Smarter Finance, Volume:58 , Issue 4, 9:1-9:7, July 2014.

10. Yang, H., Hosking, J. and Amemiya, Y., Dynamic Latent Class Model Averaging for Online Prediction. Journal of Forecasting, Volume: 34, Issue 1, 1-14, 2015.

11. Yang, P., Yang, H., Fu, H. and He, J., Jointly Modeling Label and Feature Heterogeneity in Medical Informatics, Transactions on Knowledge Discovery from Data (TKDD), Volume 10 Issue 4, 2016.

12. Yang, H., Ormandi, R., Tsao, H. and Lu, Q., Estimating Conversion Rates of Rare Events Through a Multidimensional Dynamic Hierarchical Bayesian Framework, Applied Stochastic Models in Business and Industry, Volume 32 Issue 3, 2016.

13. Huang, H., Dong, Y., Tang, J., Yang, H., Chawla, N. and Fu, X., Will Triadic Closure Strengthen Ties in Social Networks?, ACM Transactions on Knowledge Discovery from Data (TKDD), 2017.

14. Cen, Y. , Zhang, J., Yang, H. and Tang, J., Trust Prediction in Alibaba E-Commerce Platform, ACM Transactions on Knowledge and Data Engineering (TKDE), 2019.

15. Yang, H. and Zhou, J., The Study of Cognitive Graph and its Practice in E-Commerce, Communications for China Computer Federation (CCCF), 2020.

16. Ji, Yu., Yin, M., Yang, H., Zhou, J., Zheng, V., Shi, C. and Fang, Y. Accelerating Large-Scale Heterogeneous Interaction Graph Embedding Learning via Importance Sampling, ACM Transactions on Knowledge Discovery from Data (TKDD), 2020.

17. Zhen, Yang, Yang, H. and Tang, J., Region or Global? A Principle for Negative Sampling in Graph-based Recommendation, ACM Transactions on Knowledge and Data Engineering (TKDE), 2022.

18. Yao, J., Zhang, S., etc, Fei, Wu and Yang, H., Edge-Cloud Polarization and Collaboration: A

Comprehensive Survey for AI, ACM Transactions on Knowledge and Data Engineering (TKDE), 2022.

Conferences:

1. Zhou, M., Yang, H., Zhou, Y., Dunson, D.B., Sapiro, G. and Carin, L., The Dependent Hierarchical Beta Process and Covariate-dependent Sparse Image Analysis. International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2011.

2. Zhou, M., Yang, H., Zhou, Y., Dunson, D.B., Sapiro, G. and Carin, L., Landmark Dependent Hierarchical Beta Process for Robust Sparse Factor Analysis. International Conference on Machine Learning (ICML) 2011 Structured Sparsity Workshop, Bellevue.

3. Zhou, M., Yang, H., Zhou, Y., Dunson, D.B., Sapiro, G. and Carin, L., Dependent Hierarchical Beta Process for Image Interpolation and Denoising. Fourteenth International Conference on Arti.cial Intelligence and Statistics. (AISTATS) 2011.

4. Yang, H. and He, J., NOTAM^2: Nonparametric Bayes multi-task multi-view learning, World Statistics Conference (WSC), 2013.

5. Yang, H. and He, J., Learning with Dual Heterogeneity: A Nonparametric Bayes Model. 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2014.

6. Yang, P., He, J., Yang, H. and Fu, H., Learning from Label and Feature Heterogeneity, IEEE International Conference on Data Mining (ICDM), 2014.

7. Zhu, Y., Yang, H. and He, J., Co-Clustering based Dual Prediction for Cargo Pricing Optimization. 21th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2015.

8. Ormandi, R., Yang, H. and Lu, Q., Scalable Multidimensional Hierarchical Bayesian Modeling on Spark. 21th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) BigMine Workshop, 2015.

9. Zhang, R. and Yang, H., Dynamic Building Energy Consumption Forecast Using Weather Forecast Interpolations. IEEE SmartGridComm15 Symposium - Data Mgmt Analytics and Pricing, 2015.

10. Yang, H., Zhu, Y. and He, J., User Action Prediction for Computational Advertisement Using Local Graph Algorithms. 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) MLG Workshop, 2016.

11. Yang, H., Lu, Q., Qiu, A.X. and Han, C., Large Scale CVR Prediction through Dynamic Transfer Learning of Global and Local Features. 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) BigMine Workshop, 2016.

12. Yang, H. and Lu, Q., Meta Analyses for Dynamic Contextual Multi Arm Bandits in Display Advertisement. IEEE International Conference on Data Mining series (ICDM), 2016.

13. Yang, H., Zhu, Y. and He, J., Local Algorithm for User Action Prediction Towards Display Ads. 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2017.

14. Wang, C., Jiang, F. and Yang, H., Hybrid Framework for Text Modeling with Convolutional RNN. 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2017.

15. Yang, H., Bayesian Heteroscedastic Matrix Factorization for Conversion Rate Prediction. 26th ACM International Conference on Information and Knowledge Management (CIKM), 2017.

16. Liu, Z., Zheng, V.W., Zhao, Z., Yang, H., Chang, K., Wu, M. and Ying, J., Subgraph-augmented Path Embedding for Semantic User Search on Heterogeneous Social Network, The Web Conference (WWW), 2018.

17. Zhang, Z., Yang, H. and Zhang, J., ANRL: Attributed Network Representation Learning via Deep Neural Networks, International Joint Conference on Artificial Intelligence (IJCAI), 2018.

18. Liu, N., Yang, H. and Hu, X. Adversarial Detection with Model Interpretation. 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2018.

19. Zhou, D., He, J., Yang, H. and Fan, W. SPARC: Self-Paced Network Representation for Few-Shot Rare Category Characterization. 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2018.

20. Shen, X., Yang, H., Ester, M. and Wang, C., Mobile access record resolution on large-scale identifier-linkage graphs. 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2018.

21. Liu, Z., Zheng, V., Zhao, Z., Li, Z., Yang, H., Wu, M. and Ying, J., Interactive Paths Embedding for Semantic Proximity Search on Heterogeneous Graphs. 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2018.

22. Zhou, S., Yang, H., Ester, M. and Wang, C., PRRE: Personalized Relation Ranking Embedding for Attributed Network. 27th ACM International Conference on Information and Knowledge Management (CIKM), 2018.

23. Vincent, Z., Sha, M., Li, Y., Yang, H., Fang, Y., Zhang, Z. and Chang, K., Heterogeneous Embedding Propagation for Large-Scale E-Commerce User Alignment. IEEE International Conference on Data Mining series (ICDM), 2018.

24. Cen, Y., Zou,X., Zhang, J., Yang, H., Zhou, J. and Tang, J., Representation Learning for Attributed Multiplex Heterogeneous Network. 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019.

25. Liu, N., Tan, Q., Li, Y., Yang, H., Zhou, J. and Hu, X., Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding. 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019.

26. Chen, Q., Lin, J., Zhang, Y., Yang, H., Zhou, J. and Tang, J., Towards Knowledge-Based Personalized Product Description Generation in E-commerce. 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019.

27. Du, Z., Wang, X., Yang, H., Zhou, J. and Tang, J., Sequential Scenario-Specific Meta Learner for Online Recommendation. 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019.

28. Zhu, R., Zhao, K., Yang, H., Lin, W., Zhou, C., Ai, B., Li, Y. and Zhou, J., AliGraph: A Comprehensive Graph Neural Network Platform. 45th International Conference on Very Large Data Bases (VLDB), 2019.

29. Zhao, Y., Wang, X., Yang, H., Song, L., Tang, J., Large Scale Evolving Graphs with Burst Detection. 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019.

30. Li, C., Shen, D., Jia, K. and Yang, H., Hierarchical Representation Learning for Bipartite Graphs. 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019.

31. Ding, M., Zhou, C., Chen, Q., Yang, H. and Tang, J., Cognitive Graph for Multi-Hop Reading Comprehension at Scale. 57th Annual Meeting of the Association for Computational Linguistics (ACL), 2019.

32. Ye, Y., Wang, X., Yao, J., Jia, K., Zhou, J., Xiao, Y. and Yang, H. , Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks. 28th ACM International Conference on Information and Knowledge Management (CIKM), 2019.

33. Chen, Q., Lin, J., Zhang, Y., Ding, M., Yang, H. and Tang, J., Towards Knowledge-Based Recommender Dialog System. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019.

34. Ma, J., Zhou, C., Cui, P., Yang, H. and Zhu, W., Learning Disentangled Representations for Recommendation. Neural Information Processing Systems (NeurIPS), 2019.

35. Yuan, B., Wang, X., and Yang, H., Variational Autoencoders for Highly Multivariate Spatial Point Processes Intensities. International Conference on Learning Representations (ICLR), 2019.

36. Tan, Q., Liu, N., Zhao, X., Yang, H., Zhou, J. and Hu, X, Learning to Hash with Graph Neural Networks for Recommender Systems. The Web Conference (WWW), 2020.

37. Jiang, Z., Gao, Z., Lan, J., Yang, H., Lu, Y. and Liu, X., Task-Oriented Genetic Activation for Large-Scale Complex Heterogeneous Graph Embedding. The Web Conference (WWW), 2020.

38. Zhao, H., Yu, J., Li, Y., Wang, D., Liu, J. , Yang, H. and Wu, F., Dress like an Internet Celebrity: Fashion Retrieval in Videos. 29th International Joint Conference on Artificial Intelligence (IJCAI), 2020.

39. Huang, W., Li, Y., and Yang, H., BiANE: Bipartite Attributed Network Embedding. 43^rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2020.

40. Zhang, S., Tan, Z., Yu, J., Zhao, Z., Kuang, K., Jiang, T., Yang, H., Wu, F. and Zhou, J., Comprehensive Information Integration Modeling Framework for Video Titling. 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020.

41. Ma, J., Zhou, C., Yang, H., Cui, P., Wang, X. and Zhu, W., Disentangled Self-Supervision in Sequential Recommenders. 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020.

42. Cen, Y., Zhang, J., Zou, X., Zhou, C., Yang, H. and Tang, J., Controllable Multi-Interest Framework for Recommendation. 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020.

43. Qiu, J., Chen, Q., Dong, Y., Zhang, J., Yang, H. and Tang, J., Graph Contrastive Coding for Structural Graph Representation Pre-Training. 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020.

44. Yang, Z., Ding, M., Zhou, C., Yang, H. , Zhou, J. and Tang, J., Understanding Negative Sampling in Graph Representation Learning. 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020.

45. He, Y., Cui, P., Ma, J., Zou, H., Wang, X., Yang, H. and Yu, P.S., Learning Stable Graphs from Heterogeneous Confounded Environments. 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020.

46. Ji, Y., Yin, M., Fang, Y., Yang, H., Wang, X. and Shi, C., Temporal Heterogeneous Interaction Graph Embedding For Next-Item Recommendation. ECML-PKDD, 2020.

47. Wang, Y., Pan, G., Yao, Y., Tong, H., Yang, H., Xu, F. and Lu, J., Bringing Order to Network Embedding: A Relative Ranking based Approach. 29th ACM International Conference on Information and Knowledge Management (CIKM), 2020.

48. Zhang, S., Tan, Z., Yu, J., Zhao, Z., Kuang, K., Zhou, J., Yang, H. and Wu, F., Poet: Product-oriented Video Captioner for E-commerce. 28th ACM International Conference on Multimedia (MM), 2020.

49. Zhang, S., Jiang, T., Wang, T., Kuang, K,, Yu, J., Yang, H. and Wu, F., DeVLBert: Learning Deconfounded Visio-Linguistic Representations. 28th ACM International Conference on Multimedia (MM), 2020.

50. Chu, Y., Wang, X., Ma, J., Jia, K., Zhou, J. and Yang, H., Inductive Granger Causal Modeling for Multivariate Time Series. IEEE International Conference on Data Mining series (ICDM), 2020.

51. Ding, M., Zhou, C., Yang, H. and Tang, J., CogLTX: Applying BERT to Long Texts. Neural Information Processing Systems (NeurIPS), 2020.

52. Zou, H., Cui, P., Ma, J. and Yang, H., Counterfactual Prediction for Bundle Treatment. Neural Information Processing Systems (NeurIPS), 2020.

53. Tan, Q., Zhang, J., Yao, J.,Yang, H. and Hu, X., Sparse-Interest Network for Sequential Recommendation. 14th ACM International Conference on Web Search and Data Mining (WSDM), 2020.

54. Tan, Q., Zhang, J., Liu, N., Huang, X. ,Yang, H., Zhou, J. and Hu, X., Dynamic Memory based Attention Network for Sequential Recommendation. Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2020.

55. Wang, Q., Yao, J., Gong, C., Liu, T., Gong, M. ,Yang, H. and Han, B., Learning with Group Noise. Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2020.

56. Zheng, L., Cheng, Y., Yang, H., Cao, N. and He, J, Deep Co-attention Network for Multi-View Subspace Learning. The Web Conference (WWW), 2021.

57. Wu, Y., Lian, D., Gong, N., Yin, L, Yin, M., Zhou, J. and Yang, H., Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation, The Web Conference (WWW), 2021.

58. Wang, H., Zhou, C., Yang, C., Yang, H. and He, J., Controllable Gradient Item Retrieval. The Web Conference (WWW), 2021.

59. Ren, S., Lin, J., Zhao, G., Men, R., Yang, A., Zhou, J., Sun, X. and Yang, H., Learning Relation Alignment for Calibrated Cross-modal Retrieval. 59th Annual Meeting of the Association for Computational Linguistics (ACL), 2021.

60. Wang, P., Lin, J., Yang, A., Zhou, C., Zhang, Y., Zhou, J. and Yang, H., Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation. 59th Annual Meeting of the Association for Computational Linguistics (ACL), 2021.

61. Zhang, Z., Zhou, C., Ma, J., Zhou, J., Yang, H. and Zhao, Z., Learning to Rehearse in Long Sequence Memorization. 38th International Conference on Machine Learning (ICML), 2021.

62. Xu, J., Zhao, L., Lin, J., Sun, X., and Yang, H., KNAS: Green Neural Architecture Search. 38th International Conference on Machine Learning (ICML), 2021.

63. Zhou, C., Ma, J., Zhang, J., Zhou, J. and Yang, H., Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems. 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021.

64. Zou, X., Yin, D., Zhong, Q., Yang, H., Yang, Z. and Tang, J., Controllable Generation from Pre-trained Language Models via Inverse Prompting. 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021.

65. Lin, J., Men, R., Yang, A., Zhou, C., Zhang, Y., Wang, P., Zhou, J., Tang, J. and Yang, H., M6: Multi-Modality-to-Multi-Modality Multitask Mega-transformer for Unified Pretraining. 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021.

66. Yao, J., Wang, F., Jia, K., Han, B., Zhou, J. and Yang, H., Device-Cloud Collaborative Learning for Recommendation. 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021.

67. Ji, L., Qin, Q., Han, B. and Yang, H. , Reinforcement Learning to Optimize Lifetime Value in Cold-Start Recommendation. 30th ACM International Conference on Information and Knowledge Management (CIKM), 2021.

68. Zhang, Z., Ma, J., Zhou, C., and Yang, H. , M6-BERT: Unifying Multi-Modal Controls for Conditional Image Synthesis. Neural Information Processing Systems (NeurIPS), 2021.

69. Ding, M., Yang, Z., Hong, W., Zheng, W., Yang, H. and Tang, J., CogView: Mastering Text-to-Image Generation via Transformers, Neural Information Processing Systems (NeurIPS), 2021.

70. Tan, Z., Zhang, S., Kuang, K., Zhao, Z., Yu, J., Yang, H., Tang, J., Zhou, J., Wu, F., Uncovering Causal Effects of Online Short Videos on Consumer Behaviors, 15th ACM International Conference on Web Search and Data Mining (WSDM), 2021.

71. Lu, C., Yin, M., Shen, S., Ji, L., Liu, Q. and Yang, H., Deep Unified Representation for Heterogeneous Recommendation. The Web Conference (WWW), 2022.

72. Yang, Z., Ding, M., Xu, B., Yang, H. and Tang, J., STAM: A Spatiotemporal Aggregation Method for Graph Neural Network-based Recommendation. The Web Conference (WWW), 2022.

73. Zhu, J., Yao, J., Han, B. and Yang, H., Reliable Adversarial Distillation with Unreliable Teachers. International Conference on Learning Representations (ICLR), 2022.

74. Wang, P., Yang, A., Men, R., Lin, J., Bai, S., Li, Z., Ma, J., Zhou, C., Zhou, J. and Yang, H., Unifying Modalities, Tasks, and Architectures Through a Simple Sequence-to-Sequence Learning Framework. 39th International Conference on Machine Learning (ICML), 2022.

75. Huang, Y., Lin, J., Zhou, C., Yang, H. and Huang, L. Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably). 39th International Conference on Machine Learning (ICML), 2022.

76. GraphMAE: Self-Supervised Masked Graph Autoencoders. 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022.

77. Device-Cloud Collaborative Recommendation via Meta Controller. 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022.

78. OAG-LM: Towards A Unified Backbone Language Model For Academic Knowledge Services. 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022.

79. Single Stage Virtual Try-on via Deformable Attention Flows. European Conference on Computer Vision (ECCV), 2022.

80. In-N-Out Generative Learning for Dense Unsupervised Video Segmentation, 30th ACM International Conference on Multimedia(MM), 2022.

81. AD-AUG: Adversarial Data Augmentation for Counterfactual Recommendation, ECML PKDD, 2022.

Tech Reports:

1. Yang, H., Chen, D., Wang, L., Zheng, Y. and Lu, Q., Bayesian Reinforcement Learning for Word Representations through Co-clustering with Applications on CPA Prediction. Yahoo! Tech Pulse, 2015.

2. Yang, H., Chen, D., and Wagh, E., Demographic Prediction Through Jointly Modelling of First and Third Party Data. Yahoo! Tech Pulse, 2016.