Automatic Recognition of Space-Time Constellations by Learning on the Grassmann Manifold (Extended Version). - Yuqing Du; Guangxu Zhu; Jiayao Zhang 001; Kaibin Huang (CoRR, 2018)

Grassmannian Learning: Embedding Geometry Awareness in Shallow and Deep Learning. - Jiayao Zhang 001; Guangxu Zhu; Robert W. Heath Jr.; Kaibin Huang (CoRR, 2018)

Automatic Recognition of Space-Time Constellations by Learning on the Grassmann Manifold. - Yuqing Du; Guangxu Zhu; Jiayao Zhang 001; Kaibin Huang (IEEE Trans. Signal Process., 2018)

Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations. - Jiayao Zhang 001; Hua Wang; Weijie J. Su (NeurIPS, 2021)

Some Reflections on Drawing Causal Inference using Textual Data: Parallels Between Human Subjects and Organized Texts. - Bo Zhang; Jiayao Zhang 001 (CLeaR, 2022)

ROCK: Causal Inference Principles for Reasoning about Commonsense Causality. - Jiayao Zhang 001; Hongming Zhang 009; Weijie J. Su; Dan Roth 001 (ICML, 2022)

Causal Inference Principles for Reasoning about Commonsense Causality. - Jiayao Zhang 001; Hongming Zhang 009; Dan Roth 001; Weijie J. Su (CoRR, 2022)

Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach. - Jiayao Zhang 001; Hongming Zhang 009; Zhun Deng; Dan Roth 001 (CoRR, 2022)

COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective. - Zhaowei Wang 003; Quyet V. Do; Hongming Zhang 009; Jiayao Zhang 001; Weiqi Wang 001; Tianqing Fang; Yangqiu Song; Ginny Y. Wong; Simon See (ACL (1), 2023)

FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data. - Zhun Deng; Jiayao Zhang 001; Linjun Zhang; Ting Ye; Yates Coley; Weijie J. Su; James Zou 001 (ICLR, 2023)

Estimating the Causal Effect of Early ArXiving on Paper Acceptance. - Yanai Elazar; Jiayao Zhang 001; David Wadden; Bo Zhang; Noah A. Smith (CLeaR, 2024)

Event Causality Identification with Synthetic Control. - Haoyu Wang 005; Fengze Liu; Jiayao Zhang 001; Dan Roth 001; Kyle Richardson 001 (EMNLP, 2024)

Analysis of the ICML 2023 Ranking Data: Can Authors' Opinions of Their Own Papers Assist Peer Review in Machine Learning? - Buxin Su; Jiayao Zhang 001; Natalie Collina; Yuling Yan; Didong Li; Kyunghyun Cho; Jianqing Fan; Aaron Roth 001; Weijie J. Su (CoRR, 2024)