Research: Management sciences

Qihang Lin
Lee, J. D., Lin, Q., Ma, T. & Yang, T. (2017). Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement. Journal of Machine Learning Research, 18(122), 1-43.
Qihang Lin
Xu, Y., Liu, M., Yang, T. & Lin, Q. (2017). ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization. Neural Information Processing Systems (NIPS).
Qihang Lin
Xu, Y., Lin, Q. & Yang, T. (2017). Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter. Neural Information Processing Systems (NIPS).
Xun Zhou
Yuan, Z., Zhou, X., Yang, T., Tamerius, J. & Mantilla, R. (In Press). Predicting Traffic Accidents Through Heterogeneous Urban Data: A Case Study. 6th International Workshop on Urban Computing (UrbComp) in Conjunction with ACM KDD 2017.
Johannes Ledolter
Wang, J. K., Kardon, R. H., Ledolter, J., Sibony, P. A., Kupersmith, M. & Garvin, M. K. (2017). Peripapillary Retinal Pigment Epithelium Layer Shape Changes from Acetazolamide Treatment in the Idiopathic Intracranial Hypertension Treatment Trial. ARVO: Investigative Ophthalmology & Visual Science, 58, 2554-2565.
Qihang Lin
Yang, T., Lin, Q. & Zhang, L. (2017). A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates. International Conference on Machine Learning (ICML).
Qihang Lin
Xu, Y., Lin, Q. & Yang, T. (2017). Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence. International Conference on Machine Learning (ICML).
Xun Zhou
Khezerlou, A. V., Zhou, X., Li, L., Shafiq, Z., Liu, A. X. & Zhang, F. (2017). A Traffic Flow Approach to Early Detection of Gathering Events: Comprehensive Results. ACM Transactions on Intelligent Systems and Technology (TIST), 8(6), 74.
Tong Wang
Wang, T., Rudin, C., Velez-Doshi, F., Liu, Y., Klampfl, E. & MacNeille, P. (2017). Bayesian Rule Sets for Interpretable Classification, with Application to Context-Aware Mobile Recommender Systems. Journal of Machine Learning Research.