Yiping Lu

Orcid: 0000-0001-9459-699X

Affiliations:
  • Stanford University, Institute for Computational and Mathematical Engineering, ICME, CA, USA
  • Peking University, School of Mathematical Sciences, Beijing, China


According to our database1, Yiping Lu authored at least 21 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Orthogonal Bootstrap: Efficient Simulation of Input Uncertainty.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Statistical Spatially Inhomogeneous Diffusion Inference.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
When can Regression-Adjusted Control Variates Help? Rare Events, Sobolev Embedding and Minimax Optimality.
CoRR, 2023

When can Regression-Adjusted Control Variate Help? Rare Events, Sobolev Embedding and Minimax Optimality.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Minimax Optimal Kernel Operator Learning via Multilevel Training.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Adversarial Noises Are Linearly Separable for (Nearly) Random Neural Networks.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls.
CoRR, 2022

Importance Tempering: Group Robustness for Overparameterized Models.
CoRR, 2022

Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

An Unconstrained Layer-Peeled Perspective on Neural Collapse.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2020
CURE: Curvature Regularization for Missing Data Recovery.
SIAM J. Imaging Sci., 2020

A Mean-field Analysis of Deep ResNet and Beyond: Towards Provable Optimization Via Overparameterization From Depth.
CoRR, 2020

A Mean Field Analysis Of Deep ResNet And Beyond: Towards Provably Optimization Via Overparameterization From Depth.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network.
J. Comput. Phys., 2019

Distillation ≈ Early Stopping? Harvesting Dark Knowledge Utilizing Anisotropic Information Retrieval For Overparameterized Neural Network.
CoRR, 2019

Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View.
CoRR, 2019

You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration.
Proceedings of the 7th International Conference on Learning Representations, 2019

2018
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations.
Proceedings of the 35th International Conference on Machine Learning, 2018

PDE-Net: Learning PDEs from Data.
Proceedings of the 35th International Conference on Machine Learning, 2018


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