Research Scientist, Google DeepMind
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I am a research scientist at Google DeepMind. I am broadly interested in optimization methods for machine learning, with specific focus in model self-improvement, including but not limited to:
I obtained my Ph.D. in the School of Electrical and Computer Engineering at Cornell University in May 2022, where my Ph.D. thesis focused on resource-constrained automated machine learning (AutoML). I was advised by Prof. Madeleine Udell and had Prof. Thorsten Joachims and Prof. Kilian Q. Weinberger on my committee. From Summer 2021 to Spring 2022, I was a student researcher at Google Brain. I received a B.S. degree in physics from Fudan University in 2016.
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Long-Form Factuality in Large Language Models
Jerry Wei*, Chengrun Yang*, Xinying Song*, Yifeng Lu*, Nathan Hu, Jie Huang, Dustin Tran, Daiyi Peng, Ruibo Liu, Da Huang, Cosmo Du, Quoc V. Le
Neural Information Processing Systems (NeurIPS), 2024
[abstract] [arXiv] [code] [bib]
Large Language Models as Optimizers
Chengrun Yang*, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, Xinyun Chen*
International Conference on Learning Representations (ICLR), 2024
[abstract] [arXiv] [code] [bib]
Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization for Low-Rank Tensor Completion and Tensor Robust Principal Component Analysis
Jicong Fan, Lijun
Ding, Chengrun Yang, Zhao Zhang, Madeleine
Udell
Transactions on Machine Learning Research (TMLR), 2023
[abstract] [arXiv] [bib]
TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets
Chengrun Yang, Gabriel Bender, Hanxiao Liu, Pieter-Jan Kindermans, Madeleine Udell, Yifeng Lu, Quoc V. Le, Da Huang
Neural Information Processing Systems (NeurIPS), 2022
[abstract] [arXiv] [code] [poster] [bib]
How Low Can We Go: Trading Memory for Error in Low-Precision Training
Chengrun Yang*, Ziyang Wu*, Jerry Chee, Christopher De Sa, Madeleine
Udell
International Conference on Learning Representations (ICLR), 2022
[abstract] [arXiv] [code] [poster] [bib]
Robust Non-Linear Matrix Factorization for Dictionary Learning, Denoising, and Clustering
Jicong Fan, Chengrun Yang, Madeleine
Udell
IEEE Transactions on Signal Processing (TSP), 2021
[abstract] [arXiv] [IEEE] [bib]
TenIPS: Inverse Propensity Sampling for Tensor Completion
Chengrun Yang, Lijun
Ding, Ziyang Wu, Madeleine
Udell
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
Preliminary version at NeurIPS 2020 Workshop on Optimization for Machine Learning
[abstract] [arXiv] [code] [poster] [bib]
AutoML Pipeline Selection: Efficiently Navigating the Combinatorial Space
Chengrun Yang, Jicong Fan, Ziyang Wu, Madeleine
Udell
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020
[abstract] [pdf] [code] [ACM] [ACM version errata] [bib]
Spectral Frank-Wolfe Algorithm: Strict Complementarity and Linear Convergence
Lijun
Ding, Yingjie
Fei, Qiantong Xu, Chengrun Yang
International Conference on Machine Learning (ICML), 2020
[abstract] [arXiv] [PMLR] [bib]
OBOE: Collaborative Filtering for AutoML Model Selection
Chengrun Yang, Yuji Akimoto, Dae Won Kim, Madeleine
Udell
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019
Oral Presentation
Preliminary version at NeurIPS 2018 Workshop on Meta-Learning
[abstract] [arXiv] [pdf] [code] [ACM] [poster] [bib]
Conferences: NeurIPS, ICML, ICLR, AISTATS, NeurIPS 2018 workshop on AI in Financial Services
Journals: Transactions on Machine Learning Research (TMLR), TPAMI Special Issue on AutoML
ORIE 4741 (Fall 2017, Fall 2019, Fall 2020): Learning with Big Messy Data (Teaching Assistant)
ECE 4250 (Spring 2017): Digital Signal and Image Processing (Teaching Assistant)