Qianxiao Li

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 80

Top Topics

Stochastic Gradient

Dynamical Systems

Top Venues

J. Mach. Learn. Res.

Publications

Zhuotong Chen, Zihu Wang, Yifan Yang, Qianxiao Li, Zheng Zhang
PID Control-Based Self-Healing to Improve the Robustness of Large Language Models. Trans. Mach. Learn. Res. 2024 (2024)
Shida Wang, Zhong Li, Qianxiao Li
Inverse Approximation Theory for Nonlinear Recurrent Neural Networks. ICLR (2024)
Fusheng Liu, Qianxiao Li
From Generalization Analysis to Optimization Designs for State Space Models. CoRR (2024)
Aiqing Zhu, Qianxiao Li
DynGMA: a robust approach for learning stochastic differential equations from data. CoRR (2024)
Aiqing Zhu, Qianxiao Li
DynGMA: A robust approach for learning stochastic differential equations from data. J. Comput. Phys. 513 (2024)
Zhuotong Chen, Zihu Wang, Yifan Yang, Qianxiao Li, Zheng Zhang
PID Control-Based Self-Healing to Improve the Robustness of Large Language Models. CoRR (2024)
Jiaxi Zhao, Qianxiao Li
Mitigating distribution shift in machine learning-augmented hybrid simulation. CoRR (2024)
Danimir T. Doncevic, Alexander Mitsos, Yue Guo, Qianxiao Li, Felix Dietrich, Manuel Dahmen, Ioannis G. Kevrekidis
A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms. SIAM J. Sci. Comput. 46 (2) (2024)
Xiaoli Chen, Beatrice W. Soh, Zi-En Ooi, Eléonore Vissol-Gaudin, Haijun Yu, Kostya S. Novoselov, Kedar Hippalgaonkar, Qianxiao Li
Constructing custom thermodynamics using deep learning. Nat. Comput. Sci. 4 (1) (2024)
Sohei Arisaka, Qianxiao Li
Accelerating Legacy Numerical Solvers by Non-intrusive Gradient-based Meta-solving. CoRR (2024)
Jingpu Cheng, Qianxiao Li, Ting Lin, Zuowei Shen
Interpolation, Approximation and Controllability of Deep Neural Networks. CoRR (2023)
Haotian Jiang, Qianxiao Li, Zhong Li, Shida Wang
A Brief Survey on the Approximation Theory for Sequence Modelling. CoRR (2023)
Sohei Arisaka, Qianxiao Li
Principled Acceleration of Iterative Numerical Methods Using Machine Learning. ICML (2023)
Mengmeng Zhang, Qianxiao Li, Jijun Liu
On stability and regularization for data-driven solution of parabolic inverse source problems. J. Comput. Phys. 474 (2023)
Haotian Jiang, Qianxiao Li
Forward and Inverse Approximation Theory for Linear Temporal Convolutional Networks. CoRR (2023)
Bo Lin, Qianxiao Li, Weiqing Ren
Computing high-dimensional invariant distributions from noisy data. J. Comput. Phys. 474 (2023)
Shida Wang, Zhong Li, Qianxiao Li
Inverse Approximation Theory for Nonlinear Recurrent Neural Networks. CoRR (2023)
Nanyang Ye, Qianxiao Li, Xiao-Yun Zhou, Zhanxing Zhu
An Annealing Mechanism for Adversarial Training Acceleration. IEEE Trans. Neural Networks Learn. Syst. 34 (2) (2023)
Zhuotong Chen, Qianxiao Li, Zheng Zhang
Asymptotically Fair Participation in Machine Learning Models: an Optimal Control Perspective. CoRR (2023)
Shida Wang, Qianxiao Li
StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization. CoRR (2023)
Xiaoli Chen, Beatrice W. Soh, Zi-En Ooi, Eléonore Vissol-Gaudin, Haijun Yu, Kostya S. Novoselov, Kedar Hippalgaonkar, Qianxiao Li
Constructing Custom Thermodynamics Using Deep Learning. CoRR (2023)
Haotian Jiang, Qianxiao Li
Approximation theory of transformer networks for sequence modeling. CoRR (2023)
Yingtian Zou, Fusheng Liu, Qianxiao Li
Unraveling Model-Agnostic Meta-Learning via The Adaptation Learning Rate. ICLR (2022)
Fusheng Liu, Haizhao Yang, Soufiane Hayou, Qianxiao Li
Connecting Optimization and Generalization via Gradient Flow Path Length. CoRR (2022)
Zhuotong Chen, Qianxiao Li, Zheng Zhang
Self-Healing Robust Neural Networks via Closed-Loop Control. J. Mach. Learn. Res. 23 (2022)
Zhong Li, Haotian Jiang, Qianxiao Li
On the approximation properties of recurrent encoder-decoder architectures. ICLR (2022)
Sohei Arisaka, Qianxiao Li
Accelerating numerical methods by gradient-based meta-solving. CoRR (2022)
Zichen Zhao, Qianxiao Li
Adaptive sampling methods for learning dynamical systems. MSML (2022)
Ting Lin, Zuowei Shen, Qianxiao Li
On the Universal Approximation Property of Deep Fully Convolutional Neural Networks. CoRR (2022)
Zhong Li, Jiequn Han, Weinan E, Qianxiao Li
Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks. J. Mach. Learn. Res. 23 (2022)
Danimir T. Doncevic, Alexander Mitsos, Yue Guo, Qianxiao Li, Felix Dietrich, Manuel Dahmen, Ioannis G. Kevrekidis
A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms. CoRR (2022)
Siyu Isaac Parker Tian, Zekun Ren, Selvaraj Venkataraj, Yuanhang Cheng, Daniil Bash, Felipe Oviedo, J. Senthilnath, Vijila Chellappan, Yee-Fun Lim, Armin G. Aberle, Benjamin P. MacLeod, Fraser G. L. Parlane, Curtis P. Berlinguette, Qianxiao Li, Tonio Buonassisi, Zhe Liu
Transfer Learning for Rapid Extraction of Thickness from Optical Spectra of Semiconductor Thin Films. CoRR (2022)
Alexander E. Siemenn, Zekun Ren, Qianxiao Li, Tonio Buonassisi
Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization. CoRR (2022)
Zhuotong Chen, Qianxiao Li, Zheng Zhang
Self-Healing Robust Neural Networks via Closed-Loop Control. CoRR (2022)
Yue Guo, Felix Dietrich, Tom Bertalan, Danimir T. Doncevic, Manuel Dahmen, Ioannis G. Kevrekidis, Qianxiao Li
Personalized Algorithm Generation: A Case Study in Learning ODE Integrators. SIAM J. Sci. Comput. 44 (4) (2022)
Bo Lin, Qianxiao Li, Weiqing Ren
Computing the Invariant Distribution of Randomly Perturbed Dynamical Systems Using Deep Learning. J. Sci. Comput. 91 (3) (2022)
Fusheng Liu, Haizhao Yang, Soufiane Hayou, Qianxiao Li
From Optimization Dynamics to Generalization Bounds via Łojasiewicz Gradient Inequality. Trans. Mach. Learn. Res. 2022 (2022)
Qianxiao Li, Ting Lin, Zuowei Shen
Deep Neural Network Approximation of Invariant Functions through Dynamical Systems. CoRR (2022)
Zhuotong Chen, Qianxiao Li, Zheng Zhang
Towards Robust Neural Networks via Close-loop Control. CoRR (2021)
Bo Lin, Qianxiao Li, Weiqing Ren
Computing the Invariant Distribution of Randomly Perturbed Dynamical Systems Using Deep Learning. CoRR (2021)
Haotian Jiang, Zhong Li, Qianxiao Li
Approximation Theory of Convolutional Architectures for Time Series Modelling. CoRR (2021)
Zhong Li, Jiequn Han, Weinan E, Qianxiao Li
On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis. ICLR (2021)
Nanyang Ye, Qianxiao Li, Xiao-Yun Zhou, Zhanxing Zhu
Amata: An Annealing Mechanism for Adversarial Training Acceleration. AAAI (2021)
Nanyang Ye, Jingxuan Tang, Huayu Deng, Xiao-Yun Zhou, Qianxiao Li, Zhenguo Li, Guang-Zhong Yang, Zhanxing Zhu
Adversarial Invariant Learning. CVPR (2021)
Bo Lin, Qianxiao Li, Weiqing Ren
A Data Driven Method for Computing Quasipotentials. MSML (2021)
Chi Zhang, Qianxiao Li
Distributed optimization for degenerate loss functions arising from over-parameterization. Artif. Intell. 301 (2021)
Haotian Jiang, Zhong Li, Qianxiao Li
Approximation Theory of Convolutional Architectures for Time Series Modelling. ICML (2021)
Tian Huang, Siong Thye Goh, Sabrish Gopalakrishnan, Tao Luo, Qianxiao Li, Hoong Chuin Lau
QROSS: QUBO Relaxation Parameter Optimisation via Learning Solver Surrogates. CoRR (2021)
Tian Huang, Siong Thye Goh, Sabrish Gopalakrishnan, Tao Luo, Qianxiao Li, Hoong Chuin Lau
QROSS: QUBO Relaxation Parameter optimisation via Learning Solver Surrogates. ICDCS Workshops (2021)
Haotian Jiang, Qianxiao Li
Forward and Inverse Approximation Theory for Linear Temporal Convolutional Networks. GSI (2) (2021)
Yue Guo, Felix Dietrich, Tom Bertalan, Danimir T. Doncevic, Manuel Dahmen, Ioannis G. Kevrekidis, Qianxiao Li
Personalized Algorithm Generation: A Case Study in Meta-Learning ODE Integrators. CoRR (2021)
Zhuotong Chen, Qianxiao Li, Zheng Zhang
Towards Robust Neural Networks via Close-loop Control. ICLR (2021)
Nanyang Ye, Qianxiao Li, Xiao-Yun Zhou, Zhanxing Zhu
Amata: An Annealing Mechanism for Adversarial Training Acceleration. CoRR (2020)
Zhong Li, Jiequn Han, Weinan E, Qianxiao Li
On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis. CoRR (2020)
Yongqiang Cai, Qianxiao Li, Zuowei Shen
Optimization in Machine Learning: A Distribution Space Approach. CoRR (2020)
Bo Lin, Qianxiao Li, Weiqing Ren
A Data Driven Method for Computing Quasipotentials. CoRR (2020)
Shen Ren, Qianxiao Li, Liye Zhang, Zheng Qin, Bo Yang
Optimising Stochastic Routing for Taxi Fleets with Model Enhanced Reinforcement Learning. CoRR (2020)
Chi Zhang, Yong Sheng Soh, Ling Feng, Tianyi Zhou, Qianxiao Li
Collaborative Inference for Efficient Remote Monitoring. CoRR (2020)
Zekun Ren, Juhwan Noh, Siyu I. P. Tian, Felipe Oviedo, Guangzong Xing, Qiaohao Liang, Armin Aberle, Yi Liu, Qianxiao Li, Senthilnath Jayavelu, Kedar Hippalgaonkar, Yousung Jung, Tonio Buonassisi
Inverse design of crystals using generalized invertible crystallographic representation. CoRR (2020)
Haijun Yu, Xinyuan Tian, Weinan E, Qianxiao Li
OnsagerNet: Learning Stable and Interpretable Dynamics using a Generalized Onsager Principle. CoRR (2020)
Qianxiao Li, Bo Lin, Weiqing Ren
Computing Committor Functions for the Study of Rare Events Using Deep Learning. CoRR (2019)
Qianxiao Li, Cheng Tai, Weinan E
Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations. J. Mach. Learn. Res. 20 (2019)
Chi Zhang, Qianxiao Li, Peilin Zhao
Decentralized Optimization with Edge Sampling. IJCAI (2019)
Chi Zhang, Qianxiao Li
Distributed Optimization for Over-Parameterized Learning. CoRR (2019)
Yongqiang Cai, Qianxiao Li, Zuowei Shen
A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent. ICML (2019)
Qianxiao Li, Ting Lin, Zuowei Shen
Deep Learning via Dynamical Systems: An Approximation Perspective. CoRR (2019)
Jatin Nitin Kumar, Qianxiao Li, Karen Y. T. Tang, Tonio Buonassisi, Anibal L. Gonzalez-Oyarce, Jun Ye
Machine learning enables polymer cloud-point engineering via inverse design. CoRR (2018)
Yongqiang Cai, Qianxiao Li, Zuowei Shen
On the Convergence and Robustness of Batch Normalization. CoRR (2018)
Qianxiao Li, Cheng Tai, Weinan E
Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations. CoRR (2018)
Weinan E, Jiequn Han, Qianxiao Li
A Mean-Field Optimal Control Formulation of Deep Learning. CoRR (2018)
Bo Yang, Qianxiao Li
Dynamics of Taxi-like Logistics Systems: Theory and Microscopic Simulations. CoRR (2018)
Bo Yang, Qianxiao Li
Turn-by-turn Intelligent Manoeuvring of Driverless Taxis: A Recursive Value Model Enhanced by Reinforcement Learning. Intelligent Vehicles Symposium (2018)
Erik M. Bollt, Qianxiao Li, Felix Dietrich, Ioannis G. Kevrekidis
On Matching, and Even Rectifying, Dynamical Systems through Koopman Operator Eigenfunctions. SIAM J. Appl. Dyn. Syst. 17 (2) (2018)
Qianxiao Li, Shuji Hao
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks. CoRR (2018)
Qianxiao Li, Shuji Hao
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks. ICML (2018)
Felix P. Kemeth, Sindre W. Haugland, Felix Dietrich, Tom Bertalan, Kevin Höhlein, Qianxiao Li, Erik M. Bollt, Ronen Talmon, Katharina Krischer, Ioannis G. Kevrekidis
An Emergent Space for Distributed Data With Hidden Internal Order Through Manifold Learning. IEEE Access 6 (2018)
Qianxiao Li, Long Chen, Cheng Tai, Weinan E
Maximum Principle Based Algorithms for Deep Learning. CoRR (2017)
Qianxiao Li, Cheng Tai, Weinan E
Stochastic Modified Equations and Adaptive Stochastic Gradient Algorithms. ICML (2017)
Qianxiao Li, Long Chen, Cheng Tai, Weinan E
Maximum Principle Based Algorithms for Deep Learning. J. Mach. Learn. Res. 18 (2017)
Chu Wang, Qianxiao Li, Weinan E, Bernard Chazelle
Noisy Hegselmann-Krause systems: Phase transition and the 2R-conjecture. CDC (2016)
Qianxiao Li, Cheng Tai, Weinan E
Dynamics of Stochastic Gradient Algorithms. CoRR (2015)