Jiashuo Liu
Ph.D. Candidate @ Tsinghua CS
Visiting Student Researcher @ Stanford MS&E, Cambridge AI4Med
liujiashuo77@gmail.com
[ CV English ][CV Chinese]
—Fight against the grand and lengthy emptiness with subtle and constant efforts
Bio
Hi, Im Jiashuo! I'm a final-year Ph.D. candidate in the Department of Computer Science & Technology at Tsinghua University, advised by Prof. Peng Cui and Prof. Bo Li. I am cureently visiting Prof. Mihaela van der Schaar's group at Cambridge Centre for AI in Medicine. I was a visiting student researcher at Prof. Jose H. Blanchet's group in the Department of Management Science & Engineering at Stanford University. I work remotely with Prof. Hongseok Namkoong and Prof. Henry Lam at Columbia University.My research lies at the interface of machine learning, operations research, and health-care applications, including:
(1) Generalization under Distribution Shifts: Tools for understanding real-world distribution shifts and empirical large-scale benchmarks on real-world tabular shifts.
(2) Data Heterogeneity: Algorithms to model and exploit data heterogeneity for better generalization;
(3) Data-Driven Distributionally robust optimization: Addressing the over-pessimism problem in real applications.
Recently, I'm focusing on the robust optimization with/for LLMs, including:
(1) Leverage LLM Embeddings for Robust Tabular Prediction.
(2) Evaluating the Stability of LLMs under Potential Distribution Shifts.
Tutorials/Talks
Stability Evaluation via Distributional Perturbation Analysis
Speaker: Jiashuo Liu
INFORMS'24 Annual Meeting, Seattle, US
Advances in Data-Driven Distributionally Robust Optimization
Data Heterogeneity Analysis for Out-of-Distribution Generalization
Speaker: Peng Cui, Jiashuo Liu
CoLLAs'24 Tutorial: Conference on Lifelong Learning Agents 2024, Pisa, Italy
Model the Data Heterogeneity for Out-of-Distribution Generalization
Speaker: Peng Cui, Jiashuo Liu, Bo Li, Renzhe Xu
SDM'24 Tutorial: SIAM International Conference on Data Mining 2024, Huston, US
Modeling & Exploiting Data Heterogeneity under Distribution Shifts
Speaker: Jiashuo Liu, Tiffany (Tianhui) Cai, Peng Cui, Hongseok Namkoong
NeurIPS'23 Tutorial: Neural Information Processing Systems 2023, New Orleans, US
Selected as Favorite Papers/Presentations (9/3500+) by Two Sigma
Publications
Most recent publications on Google Scholar. * denotes equal contributions.
LLM Embeddings Improve Test-time Adaptation to Tabular Y|X-Shifts
Yibo Zeng*, Jiashuo Liu*, Henry Lam, Hongseok Namkoong
NeurIPS'24 Workshop on Table Representation Learning
Stability Evaluation of Large Language Models via Distributional Perturbation Analysis
Jiashuo Liu, Jiajin Li, Peng Cui, Jose Blanchet
NeurIPS'24 Workshop on Red Teaming GenAI
Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift
Jiayun Wu, Jiashuo Liu, Peng Cui, Zhiwei Steven Wu
NeurIPS'24: Neural Information Processing Systems 2024
On the Need of a Modeling Language for Distribution Shifts: Illustrations on Tabular Datasets
Jiashuo Liu*, Tianyu Wang*, Peng Cui, Hongseok Namkoong
INFORMS'24 Workshop on Data Science, 2024 (full paper presentation)
NeurIPS'23: Advances in Neural Information Processing Systems, Datasets and Benchmarks Track, 2023
Selected as NeurIPS Favorite Papers/Presentations (9/3500+) by Two Sigma
Under review at Operations Research
Stability Evaluation via Distributional Perturbation Analysis
(α-β order) Jose Blanchet*, Peng Cui*, Jiajin Li*, Jiashuo Liu*
ICML'24: International Conference on Machine Learning 2024
Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications
Jiashuo Liu, Jiayun Wu, Tianyu Wang, Hao Zou, Bo Li, Peng Cui
ICML'24: International Conference on Machine Learning 2024
Short version at NeurIPS'23, Workshop on Distribution Shifts
Enhancing Distributional Stability among Sub-Populations
Jiashuo Liu, Jiayun Wu, Jie Peng, Xiaoyu Wu, Yang Zheng, Bo Li, Peng Cui
AISTATS'24: International Conference on Artificial Intelligence and Statistics 2024
Towards Robust Out-of-Distribution Generalization Bounds via Sharpness
Yingtian Zou, Kenji Kawaguchi, Yingnan Liu, Jiashuo Liu, Mong-Li Lee, Wynne Hsu
ICLR'24: International Conference on Learning Representations 2024 ((Spotlight))
Measure the Predictive Heterogeneity
Jiashuo Liu*, Jiayun Wu*, Renjie Pi, Renzhe Xu, Xingxuan Zhang, Bo Li, Peng Cui
ICLR'22: International Conference on Learning Representations 2022
Distributionally Robust Learning with Stable Adversarial Training
Jiashuo Liu, Zheyan Shen, Peng Cui, Linjun Zhou, Kun Kuang, Bo Li
TKDE'22: IEEE Transactions on Knowledge and Data Engineering 2022
Distributionally Robust Optimization with Data Geometry
Jiashuo Liu*, Jiayun Wu*, Bo Li, Peng Cui
NeurIPS'22: Neural Information Processing Systems 2022 (Spotlight)
Kernelized Heterogeneous Risk Minimization
Jiashuo Liu*, Zheyuan Hu*, Peng Cui, Bo Li, Zheyan Shen
NeurIPS'21: Neural Information Processing Systems 2021
Heterogeneous Risk Minimization
Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen
ICML'21: International Conference on Machine Learning 2021
Stable Adversarial Learning under Distributional Shifts
Jiashuo Liu, Zheyan Shen, Peng Cui, Linjun Zhou, Kun Kuang, Bo Li, Yishi Lin
AAAI'21: AAAI Conference on Artificial Intelligence
Towards Out-of-Distribution Generalization: A Survey
Jiashuo Liu*, Zheyan Shen*, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, Peng Cui
Survey Paper
LLM Embeddings Improve Test-time Adaptation to Tabular Y|X-Shifts
Yibo Zeng*, Jiashuo Liu*, Henry Lam, Hongseok Namkoong
NeurIPS'24 Workshop on Table Representation Learning
Stability Evaluation of Large Language Models via Distributional Perturbation Analysis
Jiashuo Liu, Jiajin Li, Peng Cui, Jose Blanchet
NeurIPS'24 Workshop on Red Teaming GenAI
Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift
Jiayun Wu, Jiashuo Liu, Peng Cui, Zhiwei Steven Wu
NeurIPS'24: Neural Information Processing Systems 2024
On the Need of a Modeling Language for Distribution Shifts: Illustrations on Tabular Datasets
Jiashuo Liu*, Tianyu Wang*, Peng Cui, Hongseok Namkoong
INFORMS'24 Workshop on Data Science, 2024 (full paper presentation)
NeurIPS'23: Advances in Neural Information Processing Systems, Datasets and Benchmarks Track, 2023
Selected as NeurIPS Favorite Papers/Presentations (9/3500+) by Two Sigma
Under review at Operations Research
Stability Evaluation via Distributional Perturbation Analysis
(α-β order) Jose Blanchet*, Peng Cui*, Jiajin Li*, Jiashuo Liu*
ICML'24: International Conference on Machine Learning 2024
Enhancing Distributional Stability among Sub-Populations
Jiashuo Liu, Jiayun Wu, Jie Peng, Xiaoyu Wu, Yang Zheng, Bo Li, Peng Cui
AISTATS'24: International Conference on Artificial Intelligence and Statistics 2024
Towards Robust Out-of-Distribution Generalization Bounds via Sharpness
Yingtian Zou, Kenji Kawaguchi, Yingnan Liu, Jiashuo Liu, Mong-Li Lee, Wynne Hsu
ICLR'24: International Conference on Learning Representations 2024 ((Spotlight))
Enhancing Distributional Stability among Sub-Populations
Jiashuo Liu, Jiayun Wu, Jie Peng, Xiaoyu Wu, Yang Zheng, Bo Li, Peng Cui
AISTATS'24: International Conference on Artificial Intelligence and Statistics 2024
Measure the Predictive Heterogeneity
Jiashuo Liu*, Jiayun Wu*, Renjie Pi, Renzhe Xu, Xingxuan Zhang, Bo Li, Peng Cui
ICLR'22: International Conference on Learning Representations 2022
Kernelized Heterogeneous Risk Minimization
Jiashuo Liu*, Zheyuan Hu*, Peng Cui, Bo Li, Zheyan Shen
NeurIPS'21: Neural Information Processing Systems 2021
Heterogeneous Risk Minimization
Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen
ICML'21: International Conference on Machine Learning 2021
Topology-Aware Dynamic Reweighting for Distribution Shifts on Graph
Weihuang Zheng*, Jiashuo Liu*, Jiaxing Li, Jiayun Wu, Peng Cui, Youyong Kong
Preprint
Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications
Jiashuo Liu, Jiayun Wu, Tianyu Wang, Hao Zou, Bo Li, Peng Cui
ICML'24: International Conference on Machine Learning 2024
Short version at NeurIPS'23, Workshop on Distribution Shifts
Distributionally Robust Learning with Stable Adversarial Training
Jiashuo Liu, Zheyan Shen, Peng Cui, Linjun Zhou, Kun Kuang, Bo Li
TKDE'22: IEEE Transactions on Knowledge and Data Engineering 2022
Distributionally Robust Optimization with Data Geometry
Jiashuo Liu*, Jiayun Wu*, Bo Li, Peng Cui
NeurIPS'22: Neural Information Processing Systems 2022 (Spotlight)
Stable Adversarial Learning under Distributional Shifts
Jiashuo Liu, Zheyan Shen, Peng Cui, Linjun Zhou, Kun Kuang, Bo Li, Yishi Lin
AAAI'21: AAAI Conference on Artificial Intelligence
Towards Out-of-Distribution Generalization: A Survey
Jiashuo Liu*, Zheyan Shen*, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, Peng Cui
Survey Paper
LLM Embeddings Improve Test-time Adaptation to Tabular Y|X-Shifts
Yibo Zeng*, Jiashuo Liu*, Henry Lam, Hongseok Namkoong
NeurIPS'24 Workshop on Table Representation Learning
Stability Evaluation of Large Language Models via Distributional Perturbation Analysis
Jiashuo Liu, Jiajin Li, Peng Cui, Jose Blanchet
NeurIPS'24 Workshop on Red Teaming GenAI
Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift
Jiayun Wu, Jiashuo Liu, Peng Cui, Zhiwei Steven Wu
NeurIPS'24: Neural Information Processing Systems 2024
Topology-Aware Dynamic Reweighting for Distribution Shifts on Graph
Weihuang Zheng*, Jiashuo Liu*, Jiaxing Li, Jiayun Wu, Peng Cui, Youyong Kong
Preprint
On the Need of a Modeling Language for Distribution Shifts: Illustrations on Tabular Datasets
Jiashuo Liu*, Tianyu Wang*, Peng Cui, Hongseok Namkoong
INFORMS'24 Workshop on Data Science, 2024 (full paper presentation)
NeurIPS'23: Advances in Neural Information Processing Systems, Datasets and Benchmarks Track, 2023
Selected as NeurIPS Favorite Papers/Presentations (9/3500+) by Two Sigma
Under review at Operations Research
Stability Evaluation via Distributional Perturbation Analysis
(α-β order) Jose Blanchet*, Peng Cui*, Jiajin Li*, Jiashuo Liu*
ICML'24: International Conference on Machine Learning 2024
Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications
Jiashuo Liu, Jiayun Wu, Tianyu Wang, Hao Zou, Bo Li, Peng Cui
ICML'24: International Conference on Machine Learning 2024
Short version at NeurIPS'23, Workshop on Distribution Shifts
Enhancing Distributional Stability among Sub-Populations
Jiashuo Liu, Jiayun Wu, Jie Peng, Xiaoyu Wu, Yang Zheng, Bo Li, Peng Cui
AISTATS'24: International Conference on Artificial Intelligence and Statistics 2024
Domain-wise Data Acquisition to Improve Performance under Distribution Shift
Yue He, Dongbai Li, Pengfei Tian, Han Yu, Jiashuo Liu, Hao Zou, Peng Cui
ICML'24: International Conference on Machine Learning 2024
Distributionally Generative Augmentation for Fair Facial Attribute Classification
Fengda Zhang, Qianpei He, Kun Kuang, Jiashuo Liu, Long Chen, Chao Wu, Jun Xiao, Hanwang Zhang
CVPR'24: Conference on Computer Vision and Pattern Recognition 2024
Rethinking the Evaluation Protocol of Domain Generalization
Han Yu, Xingxuan Zhang, Renzhe Xu, Jiashuo Liu, Yue He, Peng Cui
CVPR'24: Conference on Computer Vision and Pattern Recognition 2024
Towards Robust Out-of-Distribution Generalization Bounds via Sharpness
Yingtian Zou, Kenji Kawaguchi, Yingnan Liu, Jiashuo Liu, Mong-Li Lee, Wynne Hsu
ICLR'24: International Conference on Learning Representations 2024 ((Spotlight))
Offline Policy Evaluation in Large Action Spaces via Outcome-Oriented Action Grouping
Jie Peng, Hao Zou, Jiashuo Liu, Shaoming Li, Yibao Jiang, Jian Pei, Peng Cui
WWW'23: The ACM Web Conference 2023
Measure the Predictive Heterogeneity
Jiashuo Liu*, Jiayun Wu*, Renjie Pi, Renzhe Xu, Xingxuan Zhang, Bo Li, Peng Cui
ICLR'22: International Conference on Learning Representations 2022
Distributionally Robust Learning with Stable Adversarial Training
Jiashuo Liu, Zheyan Shen, Peng Cui, Linjun Zhou, Kun Kuang, Bo Li
TKDE'22: IEEE Transactions on Knowledge and Data Engineering 2022
Distributionally Robust Optimization with Data Geometry
Jiashuo Liu*, Jiayun Wu*, Bo Li, Peng Cui
NeurIPS'22: Neural Information Processing Systems 2022 (Spotlight)
Towards the ultimate PMT waveform analysis for neutrino and dark matter experiments
Dacheng Xu, Benda Xu, Erjin Bao, Yiyang Wu, Aiqiang Zhang, Yuyi Wang, Geliang Zhang, Yu Xu, Ziyi Guo, Jihui Pei, Hanyang Mao, Jiashuo Liu, Zhe Wang, Shaomin Chen
JINST'22: Journal of Instrumentation 2022
Invariant Preference Learning for General Debiasing in Recommendation
Zimu Wang, Yue He, Jiashuo Liu, Wenchao Zou, Philip Yu, Peng Cui
KDD'22: SIGKDD Conference on Knowledge Discovery and Data Mining 2022
Kernelized Heterogeneous Risk Minimization
Jiashuo Liu*, Zheyuan Hu*, Peng Cui, Bo Li, Zheyan Shen
NeurIPS'21: Neural Information Processing Systems 2021
Heterogeneous Risk Minimization
Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen
ICML'21: International Conference on Machine Learning 2021
Stable Adversarial Learning under Distributional Shifts
Jiashuo Liu, Zheyan Shen, Peng Cui, Linjun Zhou, Kun Kuang, Bo Li, Yishi Lin
AAAI'21: AAAI Conference on Artificial Intelligence
Triple Generative Adversarial Networks
Chongxuan Li, Kun Xu, Jun Zhu, Jiashuo Liu, Bo Zhang
TPAMI'21: Transactions on Pattern Analysis and Machine Intelligence 2021
Signed Graph Neural Network with Latent Groups
Haoxin Liu, Ziwei Zhang, Peng Cui, Yafeng Zhang, Qiang Cui, Jiashuo Liu, Wenwu Zhu
KDD'21: SIGKDD Conference on Knowledge Discovery and Data Mining 2021
Stable Learning via Differentiated Variable Decorrelation
Zheyan Shen, Peng Cui, Jiashuo Liu, Tong Zhang, Bo Li, Zhitang Chen
KDD'20: SIGKDD Conference on Knowledge Discovery and Data Mining 2020
Achievements
Excellent Comprehensive Scholarship of Tsinghua University , for Ph.D. student, 2022
National Scholarship (Nationwide, Top 1%), for Ph.D. student, 2022
Apple Scholars in AI/ML Nomination (Top-2 in Tsinghua), for Ph.D. student, 2021
Excellent Undergraduate (Top 10% in Tsinghua), 2020
Excellent Academic Scholarship , for undergraduates, 2018, 2019
Excellent Comprehensive Scholarship (Top 5% in Tsinghua), for undergraduates, 2017
Freshman Scholarship (2nd Grade) (Top 0.01% in Nei Mongol), for undergraduates, 2016
Distribution robustness adversarial learning method
Peng Cui, Jiashuo Liu, filed August 30, 2020, issued December 13, 2022.
CertificateInvariant learning method and device based on heterogeneity hybrid data
Peng Cui, Jiashuo Liu, filed April 28, 2021, and issued January 31, 2023.
CertificateServices
Journal reviewer: Operations Research, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Multimedia (TMM)Conference reviewer / Program committee: ICLR (2024), NeurIPS (2024, 2023), NeurIPS Datasets & Benchmark (2024), ICML (2022,2023,2024), UAI (2022,2023,2024), AAAI (2022), IJCAI (2022,2023), CVPR (2022,2023, 2024), ECCV (2024), ICCV (2023), CoLLAs (2022,2023,2024), AISTATS (2021,2023,2024), ICDM (2024), NeurIPS DistShift (2023)
Teaching:
Machine Learning Summer Camp for Primary & Middle School Students at Cambridge University (Summer 2024)
Software Engineering at Tsinghua University (Fall 2019, 2020, 2021, 2022, Spring 2022, 2023 TA)
Object-oriented Programming at Tsinghua University (Summer 2022, TA)
Vitæ
Full Resume in PDF.
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Cambridge University June 2024 - Nov 2024Visiting Student Researcher
AI for Medicine Center
with Prof. Mihaela van der Schaar -
Stanford University Oct 2023 - Apr 2024Visiting Student Researcher
Management Science and Engineering
with Prof. Jose Blanchet -
Columbia Business School Jan 2023 - 2024Visiting Student Researcher (remote)
Decision, Risk, and Operations division
with Prof. Hongseok Namkoong -
Tecent Summer 2022Research Scientist, Intern
Wechat Group -
Tsinghua University 2020 - 2025Ph.D. Student
Computer Science
with Prof. Peng Cui and Bo Li -
Sogou July-Sep 2021Research Assistant, Intern
Search Engine Group -
Tsinghua University 2016 - 2020B.Sc. Student
Computer Science and Technology
Excellent Undergraduate (Top 10%)