Find me on google scholar.
I'm broadly interested in machine learning, causal inference, and computer architecture. Recently, I've developed interests in quantum computing.
G. Basse, Y. Ding, P. Toulis. Minimax Crossover Designs.
Y. Ding, P. Toulis. Dynamical Systems Theory for Causal Inference with Application to Synthetic Control Methods.
Y. Ding, N. Mishra, H. Hoffmann. Generative and Multi–phase Learning for Computer Systems Optimization.
In Proceedings of the International Symposium on Computer Architecture (ISCA), June, 2019, Phoenix, AZ, USA.
Y. Ding, R. Kondor, J. Eskreis-Winkler. Multiresolution Kernel Approximation for Gaussian Process Regression.
In Proceedings of the Neural Information Processing Systems (NIPS), December, 2017, Long Beach, USA. (Spotlight)
[Paper] [arXiv] [
Y. Ding, C. Liu, P. Zhao, S. Hoi. Large Scale Kernel Methods for Online AUC Maximization.
In Proceedings of the IEEE International Conference on Data Mining (ICDM), November, 2017, New Orleans, USA. (Long Oral)
Y. Ding, P. Zhao, S. Hoi, Y. Ong. An Adaptive Gradient Method for Online AUC Maximization.
In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), January, 2015, Austin, USA. (Oral)
P. Wu, Y. Ding, P. Zhao, C. Miao, S. Hoi. Learning Relative Similarity by Stochastic Dual Coordinate Ascent.
In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), July, 2014, Quebec City, Canada.
TA at University of Chicago: