Find me on google scholar.
I'm broadly interested in machine learning, causal inference, and computer systems and architecture. Recently, I also develop 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. To appear in the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020).
Y. Ding, N. Mishra, H. Hoffmann. Generative and Multi–phase Learning for Computer Systems Optimization.
In the 46th International Symposium on Computer Architecture (ISCA 2019).
Y. Ding, R. Kondor, J. Eskreis-Winkler. Multiresolution Kernel Approximation for Gaussian Process Regression.
In the 31st Conference on Neural Information Processing Systems (NeurIPS 2017). (Spotlight)
[Paper] [arXiv] [
Y. Ding, C. Liu, P. Zhao, S. Hoi. Large Scale Kernel Methods for Online AUC Maximization.
In the IEEE International Conference on Data Mining (ICDM 2017). (Long Oral)
Y. Ding, P. Zhao, S. Hoi, Y. Ong. An Adaptive Gradient Method for Online AUC Maximization.
In the 29th AAAI Conference on Artificial Intelligence (AAAI 2015). (Oral)
P. Wu, Y. Ding, P. Zhao, C. Miao, S. Hoi. Learning Relative Similarity by Stochastic Dual Coordinate Ascent.
In the 28th AAAI Conference on Artificial Intelligence (AAAI 2014).
TA at University of Chicago: