Xiang Wang

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Rong GeDuke UniversityVerified email at cs.duke.edu
Chenwei WuPh.D. student in Computer Science, Duke UniversityVerified email at cs.duke.edu
Rohith KuditipudiStanford UniversityVerified email at stanford.edu
Zhize LiAssistant Professor, Singapore Management UniversityVerified email at smu.edu.sg
Mo ZhouDuke UniversityVerified email at cs.duke.edu
Zhiyuan LiAssistant Professor, Toyota Technological Institute at ChicagoVerified email at ttic.edu
Holden LeeAssistant Professor of Applied Mathematics and Statistics, Johns Hopkins UniversityVerified email at jhu.edu
Wei HuAssistant Professor of Computer Science and Engineering, University of MichiganVerified email at umich.edu
Sanjeev AroraProfessor of Computer Science, Princeton UniversityVerified email at cs.princeton.edu
Yi ZhangSenior Researcher at Microsoft Research RedmondVerified email at microsoft.com
Muthu ChidambaramDuke UniversityVerified email at cs.duke.edu
Weiyao WangFAIR, Duke UniversityVerified email at fb.com
Zixuan WangPrinceton UniversityVerified email at princeton.edu
Xingyu ZhuPrinceton UniversityVerified email at princeton.edu
Fan WuProfessor, Department of Computer Science and Engineering, Shanghai Jiao Tong UniversityVerified email at cs.sjtu.edu.cn
Shuai YuanTikTok, Duke UniversityVerified email at tiktok.com
Yunwei RenPrinceton UniversityVerified email at princeton.edu
Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonVerified email at cs.washington.edu
Yuandong TianResearch Scientist, Meta AI (FAIR)Verified email at fb.com
Xinlei ChenFAIR, MetaVerified email at meta.com

Xiang Wang

Meta, Duke University

Verified email at meta.com

Deep Learning Theory Non-Convex Optimization


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets R Kuditipudi, X Wang, H Lee, Y Zhang, Z Li, W Hu, R Ge, S Arora Neural Information Processing Systems (NeurIPS 2019), 2019	86	2019
Learning two-layer neural networks with symmetric inputs R Ge, R Kuditipudi, Z Li, X Wang International Conference on Learning Representations (ICLR 2019), 2018	62	2018
Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization R Ge, Z Li, W Wang, X Wang Conference on Learning Theory (COLT 2019), 2019	34	2019
Understanding Edge-of-Stability Training Dynamics with a Minimalist Example X Zhu, Z Wang, X Wang, M Zhou, R Ge International Conference on Learning Representations (ICLR 2023), 2022	29	2022
Understanding Deflation Process in Over-parametrized Tensor Decomposition R Ge, Y Ren, X Wang, M Zhou Neural Information Processing Systems (NeurIPS 2021), 2021	20	2021
Guarantees for Tuning the Step Size using a Learning-to-Learn Approach X Wang, S Yuan, C Wu, R Ge International Conference on Machine Learning (ICML 2021), 2020	20	2020
Towards Understanding the Data Dependency of Mixup-style Training M Chidambaram, X Wang, Y Hu, C Wu, R Ge International Conference on Learning Representations (ICLR 2022), 2021	18	2021
Towards demystifying representation learning with non-contrastive self-supervision X Wang, X Chen, SS Du, Y Tian arXiv preprint arXiv:2110.04947, 2021	18	2021
Beyond Lazy Training for Over-parameterized Tensor Decomposition X Wang, C Wu, JD Lee, T Ma, R Ge Neural Information Processing Systems (NeurIPS 2020), 2020	14	2020
Strategy-proof data auctions with negative externalities X Wang, Z Zheng, F Wu, X Dong, S Tang, G Chen International Conference on Autonomous Agents & Multiagent Systems (AAMAS 2016), 2016	7	2016
Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup M Chidambaram, X Wang, C Wu, R Ge International Conference on Machine Learning (ICML 2023), 2022	4	2022
Plateau in Monotonic Linear Interpolation--A" Biased" View of Loss Landscape for Deep Networks X Wang, AN Wang, M Zhou, R Ge International Conference on Learning Representations (ICLR 2023), 2022	4	2022
Efficient auctions with identity-dependent negative externalities C Zhang, X Wang, F Wu, X Bei International Conference on Autonomous Agents & Multiagent Systems (AAMAS 2018), 2018	4	2018

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