| 2025 | ICLR | Error-quantified Conformal Inference for Time Series. | Junxi Wu, Dongjian Hu, Yajie Bao, Shu-Tao Xia, Changliang Zou |
| 2025 | ICML | Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach. | Qian Peng, Yajie Bao, Haojie Ren, Zhaojun Wang, Changliang Zou |
| 2024 | FUSION | RNN-UKF: Enhancing Hyperparameter Auto-Tuning in Unscented Kalman Filters through Recurrent Neural Networks. | Zhengyang Fan, Dan Shen, Yajie Bao, Khanh Pham, Erik Blasch, Genshe Chen |
| 2024 | ICML | Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective. | Yajie Bao, Michael Crawshaw, Mingrui Liu |
| 2023 | ICLR | EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data. | Michael Crawshaw, Yajie Bao, Mingrui Liu |
| 2022 | ICML | Fast Composite Optimization and Statistical Recovery in Federated Learning. | Yajie Bao, Michael Crawshaw, Shan Luo, Mingrui Liu |
| 2022 | UAI | Byzantine-tolerant distributed multiclass sparse linear discriminant analysis. | Yajie Bao, Weidong Liu, Xiaojun Mao, Weijia Xiong |
| 2021 | AISTATS | One-Round Communication Efficient Distributed M-Estimation. | Yajie Bao, Weijia Xiong |
| 2019 | ICIP | An Information-Theoretic Approach to Transferability in Task Transfer Learning. | Yajie Bao, Yang Li, Shao-Lun Huang, Lin Zhang, Lizhong Zheng, Amir Zamir, Leonidas J. Guibas |