Yu Gui
Welcome to my homepage!
I am a PhD candidate in the Department of Statistics at the University of Chicago. I am fortunate to be advised by Professor Rina Foygel Barber and Professor Cong Ma.
My research broadly focuses on theory and methodology in scenarios where the distribution is weakly specified or fully supervised data is unavailable.
Prior to PhD, I graduated from School of the Gifted Young at University of Science and Technology of China and was a student research intern advised by Professor Jun S Liu at Harvard.
news
| Jul 10, 2024 | A new preprint iso-DRL on how to utilize side information (e.g. shape constraints) to relieve the over-conservativeness of distributionally robust learning! As an application, iso-DRL suggests a robust approach to calibrate estimated density ratios in reweighting approaches. |
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| Jun 30, 2024 | I’m thrilled to be awarded the William Rainey Harper Dissertation Fellowship for 2024-25. |
| May 16, 2024 | A new preprint on when to deploy foundation model outputs with statistical guarantees: Conformal Alignment. |
| Dec 31, 2023 | Presented conformalized matrix completion at NeurIPS 2023 and ICSDS 2023. |
| Mar 30, 2023 | Received IMS Hannan graduate student award for our work on theory of contrastive learning. |