My research interest is in the broad area of machine learning. The focus of my doctoral work is on learning Mixed Membership Latent Variable Models (sometimes known as topic modeling) that can characterize complex high-dimensional observations including text documents (e.g., Twitter streaming, news articles, etc.), user preferences (pairwise comparisons over products, movies, etc.). Our focus is to develop provable algorithms with computation and sample complexity guarantees under an inevitable separable condition. Please find my dissertation here.
Projects
Publications
2016
- W. Trouleau, A. Ashkan, W. Ding, and B. Eriksson, “Just One More: Modeling Binge Watching Behavior”, to appear in SIGKDD, Aug., 2016. [Oral Presentation]
- W. Ding, P. Ishwar, and V. Saligrama, “Necessary and Sufficient Conditions and a Provably Efficient Algorithm for Separable Topic Discovery,” in IEEE Journal on Special Topics in Signal Processing (JSTSP), Special Issue on Structured Matrices in Signal and Data Processing. arXiv version: arXiv:1508.05565 [cs.LG].
- L. Guo, C. Vargo, Z. Pan, W. Ding, and P. Ishwar, “Big Social Data Analytics in Journalism and Mass Communication: Comparing Dictionary-based Text Analysis and Unsupervised Topic Modeling,” in Journalism and Mass Communication Quarterly, 2016. [pdf]
2015
- W. Ding, P. Ishwar, and V. Saligrama, “Learning Mixed Membership Mallows Models from Pairwise Comparisons,” in arXiv:1504.00757 [cs. LG]
- W. Ding, P. Ishwar, and V. Saligrama, “A Topic Modeling Approach to Ranking,” in Proc. International Conference in Artificial Intelligence and Statistics (AISTATS’15), San Diego, CA, USA, 7-9, May., 2015. [arXiv version]
- W. Ding, P. Ishwar, and V. Saligrama, “Most Large Topic Models are Approximately Separable,” in Proc. 10th IEEE International Workshop on Information Theory and Applications (ITA), San Diego, CA, 1-6, Feb., 2015.
- W. Ding, P. Ishwar, and V. Saligrama, “Learning Shared Rankings From Mixtures of Noisy Pairwise Comparisons,” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’15), Apr., 2015.
2014
- W. Ding, P. Ishwar, and V. Saligrama, “A Topic Modeling Approach to Rank Aggregation,” in Advances in Neural Information Processing Systems (NIPS’14), Workshop on Analysis of Rank Data: Confluence of Social Choice, Operations Research, and Machine Learning, Montreal, Canada, 13, Dec., 2014. [Best Student Paper Award][Oral presentation] [arXiv version]
- W. Ding, M. H. Rohban, P. Ishwar, and V. Saligrama, “Efficient Distributed Topic Modeling with Provable Guarantees,” in Proc. International Conference on Artificial Intelligence and Statistics (AISTATS’14), Reykjavik, Iceland, 22-25, Apr., 2014, JMLR W&CP 33 :167-175
- W. Ding, P. Ishwar, V. Saligrama, and W. C. Karl, “Sensing-Aware Kernel SVM,” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’14), Florence, Italy, 4-9 May, 2014. [arXiv version]
2013
- W. Ding, P. Ishwar, M. Rohban, and V. Saligrama, “Necessary and Sufficient Conditions for Novel Word Detection in Separable Topic Models,” in Advances in Neural Information Processing Systems (NIPS’13), Workshop on Topic Models: Computation, Application, Evaluation, Lake Tahoe, NV, USA, 10 Dec., 2013. [arXiv version]
- W. Ding, P. Ishwar, and V. Saligrama, “Dynamic Topic Discovery through Sequential Projections,” in Proc. Forty-Seventh Annual Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 3-6 Nov., 2013, vol. , pp. -.
- W. Ding, M. H. Rohban, P, Ishwar, and V. Saligrama, “Topic Discovery through Data-Dependent and Random Projections,” in Proc. IEEE International Conference on Machine Learning (ICML’13), Atlanta GA, USA, 16-21 Jun., 2013, JMLR W&CP 28 (3): 1202–1210. [Oral presentation] [arXiv version] [Video]
- W. Ding, M. H. Rohban, P. Ishwar, and V. Saligrama, “A new geometric approach to latent topic modeling and discovery,” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’13), Vancouver, Canada, 26-31 May, 2013, pp. 5568-5572.