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徐易，博士，教授，博士生导师，入选国家级青年人才计划。毕业于美国爱荷华大学(The Unversity of Iowa)计算机科学系，获计算机科学专业博士学位，导师是Prof. Tianbao Yang和Prof. Qihang Lin。硕士毕业于美国南达科他州立大学，本科毕业于浙江大学数学系。加入大连理工大学之前，曾任阿里巴巴达摩院算法专家。
主要研究方向为机器学习、机器视觉、数据驱动的人工智能、统计学习理论、优化。
课题组常年招收学生，欢迎对人工智能具有浓厚兴趣的学生联系我(yxu AT dlut DOT edu DOT cn)，组内经费充足，资源丰富。 推免/考研招生专业为 人工智能、控制科学与工程；招收2025年秋季入学博士生。长期招收本科实习生、博士后。
发表论文： (全部论文见谷歌学术：https://scholar.google.com/citations?user=D4jEMqEAAAAJ&hl=en)
会议论文：
Yang Yang, Qing-Yuan Jiang, FengqiangWan, Yi Xu. Facilitating Multimodal Classification via Dynamically Learning Modality Gap. Accepted to NeurIPS 2024. (CCF A)
Qi Xu, Xuanye Fang, Yaxin Li, Jiangrong Shen, De Ma, Yi Xu, Gang Pan. RSNN: Recurrent Spiking Neural Networks for Dynamic Spatial-Temporal Information Processing. Accepted to ACM Multimedia (MM) 2024. (CCF A)
Qiran Zou, Shangyuan Yuan, Shian Du, YuWang, Chang Liu, Yi Xu, Jie Chen, Xiangyang Ji. ParCo: Part-Coordinating Text-to-Motion Synthesis. Accepted to ECCV 2024 (CCF B)
Ziquan Liu, Yufei Cui, Yan Yan, Yi Xu, Xiangyang Ji, Xue Liu, Antoni B. Chan. The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks. Accepted to ICML 2024. (CCF A)
Zihuan Qiu, Yi Xu, Fanman Meng, Hongliang Li, Linfeng Xu, Qingbo Wu. Dual-consistency Model Inversion for Non-exemplar Class Incremental Learning. Accepted to CVPR 2024. (CCF A)
Yang Yang, Yuxuan Zhang, Xin Song, Yi Xu. Not All Out-of-Distribution Data Are Harmful to Open-Set Active Learning. Accepted to NeurIPS 2023. (CCF A)
Yixiu Mao, Hongchang Zhang, Chen Chen, Yi Xu, Xiangyang Ji. Supported Value Regularization for Offline Reinforcement Learning. Accepted to NeurIPS 2023. (CCF A)
Yixiu Mao, Hongchang Zhang, Chen Chen, Yi Xu, Xiangyang Ji. Supported Trust Region Optimization for Offline Reinforcement Learning. ICML 2023. (CCF A)
Ziquan Liu, Yi Xu#, Xiangyang Ji, Antoni Chan. TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization. CVPR 2023. (CCF A)
Hongchang Zhang, Yixiu Mao, Boyuan Wang, Shuncheng He, Yi Xu, Xiangyang Ji. In-sample Actor Critic for Offline Reinforcement Learning. ICLR 2023.
Ziquan Liu, Yi Xu#, Yuanhong Xu, Qi Qian, Hao Li, Xiangyang Ji, Antoni Chan, Rong Jin. Improved Fine-Tuning by Better Leveraging Pre-Training Data. NeurIPS 2022. (CCF A)
Zhiwu Qing, Shiwei Zhang, Ziyuan Huang, Yi Xu, Xiang Wang, Mingqian Tang, Changxin Gao, Rong Jin, Nong Sang. Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency. CVPR 2022. (CCF A)
Zejiang Hou, Minghai Qin, Fei Sun, Xiaolong Ma, Kun Yuan, Yi Xu, Yen-Kuang Chen, Rong Jin, Yuan Xie, Sun-Yuan Kung. CHEX: CHannel EXploration for CNN Model Compression. CVPR 2022. (CCF A)
Xiaolong Ma, Minghai Qin, Fei Sun, Zejiang Hou, Kun Yuan, Yi Xu, Yanzhi Wang, Yen-Kuang Chen, Rong Jin, and Yuan Xie. Effective Model Sparsification by Scheduled Grow-and-Prune Methods. ICLR 2022.
Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, Tianbao Yang. An Online Method for A Class of Distributionally Robust Optimization with Non-convex Objectives. NeurIPS 2021. (CCF A)
Yi Xu, Lei Shang, Jinxing Ye, Qi Qian, Yu-Feng Li, Baigui Sun, Hao Li, Rong Jin. Dash: Semi-Supervised Learning with Dynamic Thresholding. ICML 2021. (Long Talk, acceptance rate: 3%) (CCF A)
Zhuoning Yuan*, Zhishuai Guo*, Yi Xu, Yiming Ying, Tianbao Yang. (*equal contribution) Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity. ICML 2021. (CCF A)
Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang. Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization. NeurIPS 2020. (CCF A)
Yan Yan, Yi Xu, Lijun Zhang, Xiaoyu Wang, Tianbao Yang. Stochastic Optimization for Non-convex Inf-Projection Problems. ICML 2020. (CCF A)
Yi Xu, Shenghuo Zhu, Sen Yang, Chi Zhang, Rong Jin, Tianbao Yang. Learning with Non-Convex Truncated Losses by SGD. UAI 2020. (CCF B)
Yi Xu, Rong Jin, Tianbao Yang. Non-asymptotic Analysis of Stoc hastic Methods for Non-Smooth Non-Convex Regularized Problems. NeurIPS 2019. (CCF A)
Yi Xu, Zhuoning Yuan, Sen Yang, Rong Jin, Tianbao Yang. On the Convergence of (Stochastic) Gradient Descent with Extrapolation for Non-Convex Minimization. IJCAI 2019. (CCF A)
Yi Xu, Qi Qi, Qihang Lin, Rong Jin, Tianbao Yang. Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Nonasymptotic Convergence. ICML 2019. (CCF A)
Zaiyi Chen, Yi Xu, Haoyuan Hu, Tianbao Yang. Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number. ICML 2019. (CCF A)
Yi Xu, Rong Jin, Tianbao Yang. First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time. NeurIPS 2018. (CCF A)
Zaiyi Chen*, Yi Xu*, Enhong Chen, Tianbao Yang. (*equal contribution) SadaGrad: Strongly Adaptive Stochastic Gradient Methods. ICML 2018. (CCF A)
Yi Xu, Qihang Lin, Tianbao Yang. Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter. NeurIPS 2017. (CCF A)
Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang. ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization. NeurIPS 2017. (CCF A)
Yi Xu, Qihang Lin, Tianbao Yang. Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence. ICML 2017. (CCF A)
Yi Xu, Haiqing Yang, Lijun Zhang, Tianbao Yang. Efficient Non-oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee. AAAI 2017. (CCF A)
Yi Xu*, Yan Yan*, Qihang Lin, Tianbao Yang. (*equal contribution) Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than O(1/ϵ). NeurIPS 2016. (CCF A)
期刊论文：
Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang. Unified Convergence Analysis for Adaptive Optimization with Moving Average Estimator. Machine Learning, 2024. (CCF B)
Yang Yang, Nan Jiang, Yi Xu#, De-Chuan Zhan. Robust Semi-supervised Learning by Wisely Leveraging Open-set Data. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024. (CCF A)
Peixuan Ding, Yi Xu#, Xi-Ming Sun. Multi-task Learning for Aero-engine Bearing Fault Diagnosis with Limited Data. IEEE Transactions on Instrumentation and Measurement, 2024.
Peixuan Ding, Yi Xu#, Pan Qing, Xi-Ming Sun. A novel deep learning approach for intelligent bearing fault diagnosis under extremely small samples. Applied Intelligence, 2024.
Rui Jiang, Xuetao Zhang, Yisha Liu, Yi Xu, Xuebo Zhang, Yan Zhuang. Multi-agent cooperative strategy with explicit teammate modeling and targeted informative communication. Neurocomputing, 2024.
Rui Xu, Xue-Mei Dong, Weijie Li, Jiangtao Peng, Weiwei Sun, Yi Xu. DBCTNet: Double Branch Convolution-Transformer Network for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing (TGARS), 62:1-15, 2024.
Zhiwu Qing, Shiwei Zhang, Ziyuan Huang, Yi Xu, Xiang Wang, Mingqian Tang, Changxin Gao, Rong Jin, Nong Sang. Self-Supervised Learning from Untrimmed Videos via Hierarchical Consistency. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2023. (CCF A)
Qi Qi, Yi Xu, Rong Jin, Wotao Yin, Tianbao Yang. Attentional Biased Stochastic Gradient Descent. TMLR 2023.
Yi Xu, Qihang Lin, Tianbao Yang. Accelerate Stochastic Subgradient Method by Leveraging Local Growth Condition. Analysis and Applications. Vol. 17, No. 05, pp. 773-818, 2019
指导学生竞赛：
CVPR 2024 Foundational FSOD Challenge 冠军
ICCV 2023 Point Tracking冠军
CVPR 2023 Foundation Model Challenge: Cross-Modal Retrieval冠军