Dynamics of Stochastic Gradient Algorithms. - Qianxiao Li; Cheng Tai; Weinan E (CoRR, 2015)

Noisy Hegselmann-Krause systems: Phase transition and the 2R-conjecture. - Chu Wang; Qianxiao Li; Weinan E; Bernard Chazelle (CDC, 2016)

Stochastic Modified Equations and Adaptive Stochastic Gradient Algorithms. - Qianxiao Li; Cheng Tai; Weinan E (ICML, 2017)

Maximum Principle Based Algorithms for Deep Learning. - Qianxiao Li; Long Chen; Cheng Tai; Weinan E (J. Mach. Learn. Res., 2017)

An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks. - Qianxiao Li; Shuji Hao (ICML, 2018)

Turn-by-turn Intelligent Manoeuvring of Driverless Taxis: A Recursive Value Model Enhanced by Reinforcement Learning. - Bo Yang; Qianxiao Li (Intelligent Vehicles Symposium, 2018)

An Emergent Space for Distributed Data With Hidden Internal Order Through Manifold Learning. - Felix P. Kemeth; Sindre W. Haugland; Felix Dietrich; Tom Bertalan; Kevin Höhlein; Qianxiao Li; Erik M. Bollt; Ronen Talmon; Katharina Krischer; Ioannis G. Kevrekidis (IEEE Access, 2018)

A Mean-Field Optimal Control Formulation of Deep Learning. - Weinan E; Jiequn Han; Qianxiao Li (CoRR, 2018)

Dynamics of Taxi-like Logistics Systems: Theory and Microscopic Simulations. - Bo Yang; Qianxiao Li (CoRR, 2018)

On the Convergence and Robustness of Batch Normalization. - Yongqiang Cai; Qianxiao Li; Zuowei Shen (CoRR, 2018)

Machine learning enables polymer cloud-point engineering via inverse design. - Jatin Nitin Kumar; Qianxiao Li; Karen Y. T. Tang; Tonio Buonassisi; Anibal L. Gonzalez-Oyarce; Jun Ye (CoRR, 2018)

On Matching, and Even Rectifying, Dynamical Systems through Koopman Operator Eigenfunctions. - Erik M. Bollt; Qianxiao Li; Felix Dietrich; Ioannis G. Kevrekidis (SIAM J. Appl. Dyn. Syst., 2018)

A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent. - Yongqiang Cai; Qianxiao Li; Zuowei Shen (ICML, 2019)

Decentralized Optimization with Edge Sampling. - Chi Zhang 007; Qianxiao Li; Peilin Zhao (IJCAI, 2019)

Distributed Optimization for Over-Parameterized Learning. - Chi Zhang 007; Qianxiao Li (CoRR, 2019)

Computing Committor Functions for the Study of Rare Events Using Deep Learning. - Qianxiao Li; Bo Lin; Weiqing Ren (CoRR, 2019)

Deep Learning via Dynamical Systems: An Approximation Perspective. - Qianxiao Li; Ting Lin; Zuowei Shen (CoRR, 2019)

Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations. - Qianxiao Li; Cheng Tai; Weinan E (J. Mach. Learn. Res., 2019)

Collaborative Inference for Efficient Remote Monitoring. - Chi Zhang 007; Yong Sheng Soh; Ling Feng; Tianyi Zhou 007; Qianxiao Li (CoRR, 2020)

Optimization in Machine Learning: A Distribution Space Approach. - Yongqiang Cai; Qianxiao Li; Zuowei Shen (CoRR, 2020)

Inverse design of crystals using generalized invertible crystallographic representation. - 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; et al. (CoRR, 2020)

OnsagerNet: Learning Stable and Interpretable Dynamics using a Generalized Onsager Principle. - Haijun Yu; Xinyuan Tian; Weinan E; Qianxiao Li (CoRR, 2020)

Optimising Stochastic Routing for Taxi Fleets with Model Enhanced Reinforcement Learning. - Shen Ren; Qianxiao Li; Liye Zhang; Zheng Qin 004; Bo Yang (CoRR, 2020)

Amata: An Annealing Mechanism for Adversarial Training Acceleration. - Nanyang Ye 001; Qianxiao Li; Xiao-Yun Zhou 001; Zhanxing Zhu (AAAI, 2021)

Adversarial Invariant Learning. - Nanyang Ye 001; Jingxuan Tang; Huayu Deng; Xiao-Yun Zhou 001; Qianxiao Li; Zhenguo Li; Guang-Zhong Yang; Zhanxing Zhu (CVPR, 2021)

Forward and Inverse Approximation Theory for Linear Temporal Convolutional Networks. - Haotian Jiang; Qianxiao Li (GSI (2), 2021)

QROSS: QUBO Relaxation Parameter optimisation via Learning Solver Surrogates. - Tian Huang; Siong Thye Goh; Sabrish Gopalakrishnan; Tao Luo 014; Qianxiao Li; Hoong Chuin Lau (ICDCS Workshops, 2021)

Towards Robust Neural Networks via Close-loop Control. - Zhuotong Chen; Qianxiao Li; Zheng Zhang (ICLR, 2021)

On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis. - Zhong Li 004; Jiequn Han; Weinan E; Qianxiao Li (ICLR, 2021)

Approximation Theory of Convolutional Architectures for Time Series Modelling. - Haotian Jiang; Zhong Li 004; Qianxiao Li (ICML, 2021)

A Data Driven Method for Computing Quasipotentials. - Bo Lin; Qianxiao Li; Weiqing Ren (MSML, 2021)

Distributed optimization for degenerate loss functions arising from over-parameterization. - Chi Zhang 007; Qianxiao Li (Artif. Intell., 2021)

Personalized Algorithm Generation: A Case Study in Meta-Learning ODE Integrators. - Yue Guo 006; Felix Dietrich; Tom Bertalan; Danimir T. Doncevic; Manuel Dahmen; Ioannis G. Kevrekidis; Qianxiao Li (CoRR, 2021)

On the approximation properties of recurrent encoder-decoder architectures. - Zhong Li 004; Haotian Jiang; Qianxiao Li (ICLR, 2022)

Unraveling Model-Agnostic Meta-Learning via The Adaptation Learning Rate. - Yingtian Zou; Fusheng Liu; Qianxiao Li (ICLR, 2022)

Adaptive sampling methods for learning dynamical systems. - Zichen Zhao; Qianxiao Li (MSML, 2022)

Connecting Optimization and Generalization via Gradient Flow Path Length. - Fusheng Liu; Haizhao Yang; Soufiane Hayou; Qianxiao Li (CoRR, 2022)

Accelerating numerical methods by gradient-based meta-solving. - Sohei Arisaka; Qianxiao Li (CoRR, 2022)

Transfer Learning for Rapid Extraction of Thickness from Optical Spectra of Semiconductor Thin Films. - Siyu Isaac Parker Tian; Zekun Ren; Selvaraj Venkataraj; Yuanhang Cheng; Daniil Bash; Felipe Oviedo; J. Senthilnath 001; Vijila Chellappan; Yee-Fun Lim; Armin G. Aberle; Benjamin P. MacLeod; Fraser G. L. Parlane; et al. (CoRR, 2022)

Deep Neural Network Approximation of Invariant Functions through Dynamical Systems. - Qianxiao Li; Ting Lin; Zuowei Shen (CoRR, 2022)

Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization. - Alexander E. Siemenn; Zekun Ren; Qianxiao Li; Tonio Buonassisi (CoRR, 2022)

On the Universal Approximation Property of Deep Fully Convolutional Neural Networks. - Ting Lin; Zuowei Shen; Qianxiao Li (CoRR, 2022)

Self-Healing Robust Neural Networks via Closed-Loop Control. - Zhuotong Chen; Qianxiao Li; Zheng Zhang (J. Mach. Learn. Res., 2022)

Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks. - Zhong Li 004; Jiequn Han; Weinan E; Qianxiao Li (J. Mach. Learn. Res., 2022)

Computing the Invariant Distribution of Randomly Perturbed Dynamical Systems Using Deep Learning. - Bo Lin; Qianxiao Li; Weiqing Ren (J. Sci. Comput., 2022)

Personalized Algorithm Generation: A Case Study in Learning ODE Integrators. - Yue Guo 006; Felix Dietrich; Tom Bertalan; Danimir T. Doncevic; Manuel Dahmen; Ioannis G. Kevrekidis; Qianxiao Li (SIAM J. Sci. Comput., 2022)

From Optimization Dynamics to Generalization Bounds via Łojasiewicz Gradient Inequality. - Fusheng Liu; Haizhao Yang; Soufiane Hayou; Qianxiao Li (Trans. Mach. Learn. Res., 2022)

Principled Acceleration of Iterative Numerical Methods Using Machine Learning. - Sohei Arisaka; Qianxiao Li (ICML, 2023)

A Brief Survey on the Approximation Theory for Sequence Modelling. - Haotian Jiang; Qianxiao Li; Zhong Li 004; Shida Wang (CoRR, 2023)

Approximation theory of transformer networks for sequence modeling. - Haotian Jiang; Qianxiao Li (CoRR, 2023)

Asymptotically Fair Participation in Machine Learning Models: an Optimal Control Perspective. - Zhuotong Chen; Qianxiao Li; Zheng Zhang (CoRR, 2023)

Computing high-dimensional invariant distributions from noisy data. - Bo Lin; Qianxiao Li; Weiqing Ren (J. Comput. Phys., 2023)

On stability and regularization for data-driven solution of parabolic inverse source problems. - Mengmeng Zhang; Qianxiao Li; Jijun Liu (J. Comput. Phys., 2023)

An Annealing Mechanism for Adversarial Training Acceleration. - Nanyang Ye 001; Qianxiao Li; Xiao-Yun Zhou 001; Zhanxing Zhu (IEEE Trans. Neural Networks Learn. Syst., 2023)

An Optimal Control View of LoRA and Binary Controller Design for Vision Transformers. - Chi Zhang 007; Jingpu Cheng; Qianxiao Li (ECCV (53), 2024)

Inverse Approximation Theory for Nonlinear Recurrent Neural Networks. - Shida Wang; Zhong Li 004; Qianxiao Li (ICLR, 2024)

Parameter-Efficient Fine-Tuning with Controls. - Chi Zhang 007; Jingpu Cheng; Yanyu Xu; Qianxiao Li (ICML, 2024)

Accelerating Legacy Numerical Solvers by Non-intrusive Gradient-based Meta-solving. - Sohei Arisaka; Qianxiao Li (ICML, 2024)

From Generalization Analysis to Optimization Designs for State Space Models. - Fusheng Liu; Qianxiao Li (ICML, 2024)

StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization. - Shida Wang; Qianxiao Li (ICML, 2024)

Learning Macroscopic Dynamics from Partial Microscopic Observations. - Mengyi Chen; Qianxiao Li (NeurIPS, 2024)

Approximation Rate of the Transformer Architecture for Sequence Modeling. - Haotian Jiang; Qianxiao Li (NeurIPS, 2024)

Unifying back-propagation and forward-forward algorithms through model predictive control. - Lianhai Ren; Qianxiao Li (CoRR, 2024)

DynGMA: A robust approach for learning stochastic differential equations from data. - Aiqing Zhu; Qianxiao Li (J. Comput. Phys., 2024)

Constructing custom thermodynamics using deep learning. - Xiaoli Chen; Beatrice W. Soh; Zi-En Ooi; Eléonore Vissol-Gaudin; Haijun Yu; Kostya S. Novoselov; Kedar Hippalgaonkar; Qianxiao Li (Nat. Comput. Sci., 2024)

A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms. - Danimir T. Doncevic; Alexander Mitsos; Yue Guo 006; Qianxiao Li; Felix Dietrich; Manuel Dahmen; Ioannis G. Kevrekidis (SIAM J. Sci. Comput., 2024)

PID Control-Based Self-Healing to Improve the Robustness of Large Language Models. - Zhuotong Chen; Zihu Wang; Yifan Yang; Qianxiao Li; Zheng Zhang (Trans. Mach. Learn. Res., 2024)

Autocorrelation Matters: Understanding the Role of Initialization Schemes for State Space Models. - Fusheng Liu; Qianxiao Li (ICLR, 2025)

BP-Modified Local Loss for Efficient Training of Deep Neural Networks. - Lianhai Ren; Qianxiao Li (ICLR, 2025)

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images. - Aiqing Zhu; Yuting Pan; Qianxiao Li (ICLR, 2025)

Learning Parametric Koopman Decompositions for Prediction and Control. - Yue Guo 006; Milan Korda; Ioannis G. Kevrekidis; Qianxiao Li (SIAM J. Appl. Dyn. Syst., 2025)

Interpolation, Approximation, and Controllability of Deep Neural Networks. - Jingpu Cheng; Qianxiao Li; Ting Lin; Zuowei Shen (SIAM J. Control. Optim., 2025)

Mitigating Distribution Shift in Machine Learning-Augmented Hybrid Simulation. - Jiaxi Zhao; Qianxiao Li (SIAM J. Sci. Comput., 2025)

Learning task-specific predictive models for scientific computing. - Jianyuan Yin; Qianxiao Li (CoRR, 2025)

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions. - Haotian Jiang; Zeyu Bao; Shida Wang; Qianxiao Li (CoRR, 2025)

The Effect of Depth on the Expressivity of Deep Linear State-Space Models. - Zeyu Bao; Penghao Yu; Haotian Jiang; Qianxiao Li (CoRR, 2025)

A unified framework on the universal approximation of transformer-type architectures. - Jingpu Cheng; Qianxiao Li; Ting Lin; Zuowei Shen (CoRR, 2025)