ZhuSuan: A Library for Bayesian Deep Learning. - Jiaxin Shi; Jianfei Chen 001; Jun Zhu 001; Shengyang Sun; Yucen Luo; Yihong Gu; Yuhao Zhou (CoRR, 2017)

Language Modeling with Sparse Product of Sememe Experts. - Yihong Gu; Jun Yan 012; Hao Zhu 006; Zhiyuan Liu 001; Ruobing Xie; Maosong Sun 001; Fen Lin; Leyu Lin (EMNLP, 2018)

Cross Domain Imitation Learning. - Kuno Kim; Yihong Gu; Jiaming Song; Shengjia Zhao; Stefano Ermon (CoRR, 2019)

Domain Adaptive Imitation Learning. - Kuno Kim; Yihong Gu; Jiaming Song; Shengjia Zhao; Stefano Ermon (ICML, 2020)

How to Characterize The Landscape of Overparameterized Convolutional Neural Networks. - Yihong Gu; Weizhong Zhang; Cong Fang 001; Jason D. Lee; Tong Zhang 001 (NeurIPS, 2020)

Convex Formulation of Overparameterized Deep Neural Networks. - Cong Fang 001; Yihong Gu; Weizhong Zhang; Tong Zhang 001 (IEEE Trans. Inf. Theory, 2022)

Environment Invariant Linear Least Squares. - Jianqing Fan; Cong Fang 001; Yihong Gu; Tong Zhang 001 (CoRR, 2023)

The Implicit Bias of Heterogeneity towards Invariance: A Study of Multi-Environment Matrix Sensing. - Yang Xu 055; Yihong Gu; Cong Fang 001 (NeurIPS, 2024)

The Implicit Bias of Heterogeneity towards Invariance and Causality. - Yang Xu 055; Yihong Gu; Cong Fang 001 (CoRR, 2024)

Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning. - Yihong Gu; Cong Fang 001; Peter Bühlmann; Jianqing Fan (CoRR, 2024)

Open Problem: Structure-Agnostic Minimax Risk for Partial Linear Model. - Yihong Gu (COLT, 2025)

Fundamental Computational Limits in Pursuing Invariant Causal Prediction and Invariance-Guided Regularization. - Yihong Gu; Cong Fang 001; Yang Xu 055; Zijian Guo 003; Jianqing Fan (CoRR, 2025)