Yi Ding

Contact info
5730 S. Ellis Ave
John Crerar Library Building
Chicago, IL, 60637
dingy [at] uchicago [dot] edu

About

I am a 5th year Ph.D. student in Computer Science at University of Chicago, working with Hank Hoffmann and Panos Toulis. I worked closely with Risi Kondor.

Find me on google scholar.

Research interests

I'm broadly interested in machine learning, causal inference, and computer systems and architecture. Recently, I also develop interests in quantum computing.

Papers

G. Basse, Y. Ding, P. Toulis. Minimax Crossover Designs.
[arXiv]

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).
[arXiv]

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).
[Paper] [Slides] [Lightning Talk] [Poster]

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] [Spotlight Video] [Poster]

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)
[Paper]

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)
[Paper]

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).
[Paper]

Teaching experience

TA at University of Chicago: