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 architecture. Recently, I've developed 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.
[arXiv]

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

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] [Spotlight Video] [Poster]

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)
[Paper]

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)
[Paper]

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.
[Paper]

Teaching experience

TA at University of Chicago: