2024

1. [18]

   Han Yu, Xingxuan Zhang, Renzhe Xu, Jiashuo Liu, Yue He, Peng Cui Rethinking the Evaluation Protocol of Domain Generalization CVPR 2024, the Conference on Computer Vision and Pattern Recognition 2024
   [paper]
2. [17]

   Fengda Zhang, Qianpei He, Kun Kuang, Jiashuo Liu, Long Chen, Chao Wu, Jun Xiao, Hanwang Zhang Distributionally Generative Augmentation for Fair Facial Attribute Classification CVPR 2024, the Conference on Computer Vision and Pattern Recognition 2024
3. [16]

   Jiashuo Liu, Jiayun Wu, Jie Peng, Xiaoyu Wu, Yang Zheng, Bo Li, Peng Cui Enhancing Distributional Stability among Sub-populations AISTATS 2024, the 27th International Conference on Artificial Intelligence and Statistics
   [paper] [GitHub]
4. [15]

   Yingtian Zou, Kenji Kawaguchi, Yingnan Liu, Jiashuo Liu, Mong-Li Lee, Wynne Hsu Towards Robust Out-of-Distribution Generalization Bounds via Sharpness ICLR 2024, the 12th International Conference on Learning Representations.
   Spotlight presentation (Top 5%). [paper]

2023

1. [14]

   Jiashuo Liu, Jiayun Wu, Tianyu Wang, Hao Zou, Peng Cui Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications NeurIPS 2023 DistShift Workshop, in NeurIPS 2023 Workshop on Distribution Shifts.
   [workshop paper] [full paper]
2. [13]

   Jiashuo Liu*, Tianyu Wang*, Peng Cui, Hongseok Namkoong On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets NeurIPS 2023, Datasets and Benchmarks Track
   the 37th Conference on Neural Information Processing Systems.
   Selected as Favorite Papers/Presentations (9/3500+) by Two Sigma [paper][code] [python package] [downloads>2.6k]
3. [12]

   Jie Peng*, Hao Zou*, Jiashuo Liu, Shaoming Li, Yibao Jiang, Jian Pei and Peng Cui Offline Policy Evaluation in Large Action Spaces via Outcome-Oriented Action Grouping WWW 2023, the ACM Web Conference 2023. [paper]
4. [11]

   Jiashuo Liu, Jiayun Wu, Renjie Pi, Renzhe Xu, Xingxuan Zhang, Bo Li and Peng Cui Measure the Predictive Heterogeneity ICLR 2023, the 11th International Conference on Learning Representations.
   [paper]

2022

1. [10]

   Jiashuo Liu, Zheyan Shen, Peng Cui, Linjun Zhou, Kun Kuang and Bo Li Distributionally Robust Learning with Stable Adversarial Training IEEE TKDE 2022, IEEE Transactions on Knowledge and Data Engineering.
   [paper]
2. [9]

   Jiashuo Liu*, Jiayun Wu*, Bo Li and Peng Cui. Distributionally Robust Optimization with Data Geometry NeurIPS 2022, the 36th Conference on Neural Information Processing Systems.
   Spotlight presentation (Top 5.2%). [paper]
3. [8]

   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. Towards the ultimate PMT waveform analysis for neutrino and dark matter experiments JINST 2022, the Journal of Instrumentation.
   [paper]
4. [7]

   Zimu Wang, Yue He, Jiashuo Liu, Wenchao Zou, Philip Yu, Peng Cui Invariant Preference Learning for General Debiasing in Recommendation KDD 2022, the SIGKDD Conference on Knowledge Discovery and Data Mining.
   [paper]

2021

1. [6]

   Chongxuan Li, Kun Xu, Jun Zhu, Jiashuo Liu, Bo Zhang Triple Generative Adversarial Networks TPAMI 2021, the Transactions on Pattern Analysis and Machine Intelligence.
   [paper]
2. [5]

   Jiashuo Liu*, Zheyuan Hu*, Peng Cui, Bo Li, Zheyan Shen Kernelized Heterogeneous Risk Minimization NeurIPS 2021, the 35th Conference on Neural Information Processing Systems.
   [paper] [code]
3. [4]

   Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen Heterogeneous Risk Minimization ICML 2021, the 38th International Conference on Machine Learning.
   Short talk (Top 21.5%). [paper] [code]
4. [3]

   Jiashuo Liu, Zheyan Shen, Peng Cui, Linjun Zhou, Kun Kuang, Bo Li and Yishi Lin Stable Adversarial Learning under Distributional Shifts AAAI 2021, the AAAI Conference on Artificial Intelligence.
   [paper] [code]
5. [2]

   Haoxin Liu, Ziwei Zhang, Peng Cui, Yafeng Zhang, Qiang Cui, Jiashuo Liu, Wenwu Zhu Signed Graph Neural Network with Latent Groups KDD 2021, the SIGKDD Conference on Knowledge Discovery and Data Mining.
   [paper]

2020

1. [1]

   Zheyan Shen, Peng Cui, Jiashuo Liu, Tong Zhang, Bo Li, Zhitang Chen Stable Learning via Differentiated Variable Decorrelation KDD 2020, the SIGKDD Conference on Knowledge Discovery and Data Mining.
   [paper]

1. [P6]

   Jiashuo Liu, Jiayun Wu, Bo Li, Peng Cui. Predictive Heterogeneity: Measures and Applications Revise & Resubmit at JMLR,
   [paper]
2. [P5]

   Jiashuo Liu*, Zheyan Shen*, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, Peng Cui. Towards Out-of-Distribution Generalization: A Survey Under Review at IEEE TPAMI,
   [paper] [website] [pageviews>20k]
3. [P4]

   Zimu Wang, Jiashuo Liu, Hao Zou, Yue He, Dongxu Liang, Peng Cui. Exploring and Exploiting Data Heterogeneity in Recommendation Under Review at IEEE TKDE,
   [paper]
4. [P3]

   Didi Zhu, Yinchuan Li, Min Zhang, Junkun Yuan, Jiashuo Liu, Kun Kuang, Chao Wu. Understanding Prompt Tuning for V-L Models Through the Lens of Neural Collapse Under Review,
   [paper]
5. [P2]

   Zheyan Shen, Han Yu, Peng Cui, Jiashuo Liu, Xingxuan Zhang, Linjun Zhou, Furui Liu. Meta Adaptive Task Sampling for Few-Domain Generalization Under Review,
   [paper]
6. [P1]

   Xingxuan Zhang, Zekai Xu, Renzhe Xu, Jiashuo Liu, Peng Cui, Weitao Wan, Chong Sun, Chen Li. Towards Domain Generalization in Object Detection Under Review,
   [paper]

1. [W1]

   Han Yu, Jiashuo Liu, Xingxuan Zhang, Jiayun Wu, Peng Cui. A Survey on Evaluation of Out-of-Distribution Generalization Working Paper,

1. ●

   Peng Cui, Jiashuo Liu. Distribution robustness adversarial learning method. Issued 2022.

   CN Patent: CN 112085194 B, filed August 30, 2020, and issued December 13, 2022.
   [certificate]
   

2. ●

   Peng Cui, Jiashuo Liu. Invariant learning method and device based on heterogeneity hybrid data. Issued 2023.

   CN Patent: CN 113205184 B, filed April 28, 2021, and issued January 31, 2023.
   [certificate]