Shuo Yang
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PhD student, |
About me
I am a fourth-year Ph.D. student in the Department of Computer Science at the University of Texas at Austin. I am fortunate to be advised by Prof. Sujay Sanghavi. My research interests include theoretical machine learning, optimization, and sequential decision making under uncertainty.
Before moving to Austin, I received a B.E. degree in the Department of Electrical Engineering from Tsinghua University in 2018. I also spent a great summer at the University of Alberta in 2017, working on reinforcement learning with Prof. Richard Sutton. In Spring 2017, I was an exchange student at the University of Texas at Austin, and working as an intern in Prof. Peter Stone's research group.
Publications and Preprints
Toward Understanding Privileged Features Distillation in Learning-to-Rank [arXiv]
Shuo Yang, Sujay Sanghavi, Holakou Rahmanian, Jan Bakus, S.V.N. Vishwanathan
Advances in Neural Information Processing Systems (NeurIPS) 2022
Sample Efficiency of Data Augmentation Consistency Regularization [arXiv]
Shuo Yang*, Yijun Dong*, Rachel Ward, Inderjit S. Dhillon, Sujay Sanghavi, Qi Lei
Linear Bandit Algorithms with Sublinear Time Complexity [arXiv]
Shuo Yang, Tongzheng Ren, Sanjay Shakkottai, Inderjit S. Dhillon, Sujay Sanghavi
International Conference on Machine Learning (ICML) 2022
Does Preprocessing Help Training Over-parameterized Neural Networks? [paper]
Zhao Song, Shuo Yang, Ruizhe Zhang (alphabetical order)
Advances in Neural Information Processing Systems (NeurIPS) 2021
Combinatorial Bandits without Total Order for Arms [arXiv]
Shuo Yang, Tongzheng Ren, Inderjit S. Dhillon, Sujay Sanghavi
Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space [paper] [poster]
Shuo Yang*, Yanyao Shen*, Sujay Sanghavi
Advances in Neural Information Processing Systems (NeurIPS) 2019
Internships
Quant Research Intern | Jump Trading, Chicago IL
June 2022 - August 2022
Applied Scientist Intern | Amazon Search, Palo Alto CA
June 2021 - November 2021
Ranking model distillation.
Student Researcher | Google, Austin TX (remote)
June 2020 - September 2020
Accelerate training for large deep learning model (BERT).
Applied Scientist Intern | Amazon Search, Berkeley CA
June 2019 - September 2019
Customer search query understanding and inline search suggestion.
Graduate Courses
2020 Fall
EE381V Advanced Probability (Prof. Sanjay Shakkottai)
CS395T Sublinear Algorithms (Prof. Eric Price)
CS386W Wireless Networking (Prof. Lili Qiu)
2020 Spring
M 393C Markov Chains/Mixing Time (Prof. Joe Neeman)
EE 381V Statistical Machine Learning (Prof. Haris Vikalo)
2019 Fall
EE 381V Online Learning (Prof. Sanjay Shakkottai)
CS 388R Randomized Algorithm (Prof. Eric Price)
CS 388 Natural Language Processing (Prof. Greg Durrett)
CS 395T Deep Learning Seminar (Prof. Philipp Krähenbühl)
2019 Spring
EE 381V Large-Scale Optimization II (Prof. Constantine Caramanis)
CS 388G Algorithms: Techniques and Theory (Prof. Greg Plaxton)
SDS384 Theoretical Statistics (Prof. Purnamrita Sarkar)
2018 Fall
EE 381V Large-Scale Optimization I (Prof. Sujay Sanghavi)
EE 381J Probability and Stochastic Process (Prof. Sanjay Shakkottai)