Selected Publications

• Scaled ReLU Matters for Training Vision Transformers. Pichao Wang, Xue Wang, Hao Luo, Jingkai Zhou, Zhipeng Zhou, Fan Wang, Hao Li, Rong Jin. AAAI 2022.
• TransZero: Attribute-guided Transformer for Zero-Shot Learning. Shiming Chen, Zhenming Hong, Yang Liu, Guo-sen Xie, Baigui Sun, Hao Li, Qinmu Peng, Xinge You. AAAI 2022.
• An Online Method for A Class of Distributionally Robust Optimization with Non-convex Objectives. Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, Tianbao Yang. NeurIPS 2021.
• Transreid: Transformer-based object re-identification. Shuting He, Hao Luo, Pichao Wang, Fan Wang, Hao Li, Wei Jiang. ICCV 2021.
• Zen-NAS: A Zero-Shot NAS for High-Performance Image Recognition. Ming Lin, Pichao Wang, Zhenhong Sun, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, Rong Jin. ICCV 2021.
• Weakly Supervised Representation Learning with Coarse Labels. Yuanhong Xu, Qi Qian, Juhua Hu, Hao Li, Rong Jin. ICCV 2021.
• Dash: Semi-Supervised Learning with Dynamic Thresholding. Yi Xu, Lei Shang, Jinxing Ye, Qi Qian, Yu-Feng Li, Baigui Sun, Hao Li, Rong Jin. ICML 2021.
• Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity. Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang. ICML 2021.
• Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework. Qiang Zhou, Chaohui Yu, Zhibin Wang, Qi Qian, Hao Li. CVPR 2021.
• Learning Accurate Entropy Model with Global Reference for Image Compression. Yichen Qian, Zhiyu Tan, Xiuyu Sun, Ming Lin, Dongyang Li, Zhenhong Sun, Li Hao, Rong Jin. ICLR 2021.
• Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang. NeurIPS 2020.
• Stochastic Optimization for Non-convex Inf-Projection Problems. Yan Yan, Yi Xu, Lijun Zhang, Xiaoyu Wang, Tianbao Yang. ICML 2020.
• Exploiting Better Feature Aggregation for Video Object Detection. Liang Han, Pichao Wang, Zhaozheng Yin, Fan Wang, Hao Li. ACM Multimedia 2020.
• DR Loss: Improving Object Detection by Distributional Ranking. Qi Qian, Lei Chen, Hao Li, Rong Jin. CVPR 2020.
• Hierarchically Robust Representation Learning. Qi Qian, Juhua Hu, Hao Li. CVPR 2020.
• Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems. Yi Xu, Rong Jin, Tianbao Yang. NeurIPS 2019.
• Learning with Non-Convex Truncated Losses by SGD. Yi Xu, Shenghuo Zhu, Sen Yang, Chi Zhang, Rong Jin, Tianbao Yang. UAI 2019.
• On the Convergence of (Stochastic) Gradient Descent with Extrapolation for Non-Convex Optimization. Yi Xu, Zhuoning Yuan, Sen Yang, Rong Jin, Tianbao Yang. IJCAI 2019.
• Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence. Yi Xu, Qi Qi, Qihang Lin, Rong Jin, Tianbao Yang. ICML 2019.
• Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number. Zaiyi Chen, Yi Xu, Haoyuan Hu, Tianbao Yang. ICML 2019.
• SoftTriple Loss: Deep Metric Learning without Triplet Sampling. Qi Qian，Lei Shang，Baigui Sun, Juhua Hu，Hao Li，Rong Jin. ICCV 2019.
• Learning to rank proposals for object detection. Zhiyu Tan, Xuecheng Nie, Qi Qian, Nan Li, Hao Li. ICCV 2019.
• Robust Optimization over Multiple Domains. Qi Qian，Shenghuo Zhu, Jiasheng Tang， Baigui Sun，Hao Li，Rong Jin. AAAI 2019.
• Which Factorization Machine Modeling is Better: A Theoretical Answer with Optimal Guarantee. Ming Lin, Shuang Qiu, Jieping Ye, Xiaomin Song, Qi Qian, Liang Sun, Shenghuo Zhu, Rong Jin. AAAI 2019.
• Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement. Ming Lin, Xiaomin Song, Qi Qian, Hao Li, Liang Sun, Shenghuo Zhu, Rong Jin. KDD 2019.
• Large-scale Distance Metric Learning with Uncertainty. Qi Qian, Jiasheng Tang, Hao Li, Shenghuo Zhu, Rong Jin. CVPR 2018.
• First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time. Yi Xu, Rong Jin, Tianbao Yang. NeurIPS 2018.
• SADAGRAD: Strongly Adaptive Stochastic Gradient Methods. Zaiyi Chen, Yi Xu, Enhong Chen, Tianbao Yang. ICML 2018.

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