Qianxiao Li
29 papers · 2017–2025 · 8 conferences · across top CS/AI conferences
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Conferences
ICML (10)
ICLR (8)
JMLR (5)
NIPS (2)
AAAI (1)
CVPR (1)
ECCV (1)
IJCAI (1)
Top co-authors
Keywords
optimal control
(3)
adversarial training
(2)
approximation theory
(2)
stochastic modified equation
(2)
adversarial attack
(2)
recurrent neural network
(2)
dynamical system
(2)
stochastic differential equation
(2)
weak approximation
(2)
pontryagin maximum principle
(2)
stochastic gradient descent
(2)
batch normalization
(1)
robust optimization
(1)
universal approximation
(1)
attention mechanism
(1)
model robustness
(1)
transformer architecture
(1)
sparse learning
(1)
deep learning
(1)
domain generalization
(1)
Papers
From Weight-Based to State-Based Fine-Tuning: Further Memory Reduction on LoRA with Parallel Control
ICML 2025
Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images
ICLR 2025
Autocorrelation Matters: Understanding the Role of Initialization Schemes for State Space Models
ICLR 2025
BP-Modified Local Loss for Efficient Training of Deep Neural Networks
ICLR 2025
Learning Macroscopic Dynamics from Partial Microscopic Observations
NIPS 2024
Approximation Rate of the Transformer Architecture for Sequence Modeling
NIPS 2024
An Optimal Control View of LoRA and Binary Controller Design for Vision Transformers
ECCV 2024
Inverse Approximation Theory for Nonlinear Recurrent Neural Networks
ICLR 2024
Accelerating Legacy Numerical Solvers by Non-intrusive Gradient-based Meta-solving
ICML 2024
From Generalization Analysis to Optimization Designs for State Space Models
ICML 2024
StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization
ICML 2024
Parameter-Efficient Fine-Tuning with Controls
ICML 2024
Deep Neural Network Approximation of Invariant Functions through Dynamical Systems
JMLR 2024
Principled Acceleration of Iterative Numerical Methods Using Machine Learning
ICML 2023
Self-Healing Robust Neural Networks via Closed-Loop Control
JMLR 2022
Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks
JMLR 2022
Unraveling Model-Agnostic Meta-Learning via The Adaptation Learning Rate
ICLR 2022
On the approximation properties of recurrent encoder-decoder architectures
ICLR 2022
Adversarial Invariant Learning
CVPR 2021
Approximation Theory of Convolutional Architectures for Time Series Modelling
ICML 2021
Amata: An Annealing Mechanism for Adversarial Training Acceleration
AAAI 2021
On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis
ICLR 2021
Towards Robust Neural Networks via Close-loop Control
ICLR 2021
A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent
ICML 2019
Decentralized Optimization with Edge Sampling
IJCAI 2019
Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations
JMLR 2019
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks
ICML 2018
Maximum Principle Based Algorithms for Deep Learning
JMLR 2018
Stochastic Modified Equations and Adaptive Stochastic Gradient Algorithms
ICML 2017